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ORCIDInternational Journal of Imaging Systems and Technology, Volume 33
Volume 33, Number 1, January 2023
Editorial
- Mohamed L. Seghier:
Preparing a successful Special Issue proposal. 3-5
Research Articles
- Lu Ma
, Shuni Song, Liting Guo, Wenjun Tan, Lisheng Xu:
COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet. 6-17 - Harshal A. Sanghvi, Riki H. Patel, Ankur Agarwal, Shailesh Gupta, Vivek Sawhney, Abhijit S. Pandya:
A deep learning approach for classification of COVID and pneumonia using DenseNet-201. 18-38 - Harsh Gupta, Naman Bansal, Swati Garg, Hritesh Mallik, Anju Prabha, Jyoti Yadav:
A hybrid convolutional neural network model to detect COVID-19 and pneumonia using chest X-ray images. 39-52 - Pu Yan, Gang Wang, Jie Chen, Qingwei Tang, Heng Xu:
Skin lesion classification based on the VGG-16 fusion residual structure. 53-68 - Oznur Ozaltin, Orhan Coskun, Ozgur Yeniay, Abdulhamit Subasi:
Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm. 69-91 - Sumod Sundar, Sumathy Subramanian:
An effective deep learning model for grading abnormalities in retinal fundus images using variational auto-encoders. 92-107 - Uma Yadav, Ashish K. Sharma:
A novel automated depression detection technique using text transcript. 108-122 - Debayan Bhattacharya, Dennis Eggert, Christian Betz, Alexander Schlaefer:
Squeeze and multi-context attention for polyp segmentation. 123-142 - Tongyuan Huang, Yao Liu:
Research on the magnetic resonance imaging brain tumor segmentation algorithm based on DO-UNet. 143-157 - Yibeltal Tamyalew, Ayodeji Olalekan Salau, Aleka Melese Ayalew:
Detection and classification of large bowel obstruction from X-ray images using machine learning algorithms. 158-174 - Seenia Francis, Goutham Pooloth, Sai Bala Subrahmanyam Singam, Niyas Puzhakkal, Pournami Pulinthanathu Narayanan, Jayaraj Pottekkattuvalappil Balakrishnan:
SABOS-Net: Self-supervised attention based network for automatic organ segmentation of head and neck CT images. 175-191 - Bohan Yin, Shengsheng Wang, Shuzhen Lu, Guangyao Wang, Liyan Dong:
Error analysis driven network modification for surgical tools detection in laparoscopic frames. 192-203 - Mili Rosline Mathews, Sharafudeen Thaha Mohammed Anzar:
A lightweight deep learning model for retinal optical coherence tomography image classification. 204-216 - Tiejun Yang, Xinhao Bai, Xiaojuan Cui, Yuehong Gong, Lei Li:
DAU-Net: An unsupervised 3D brain MRI registration model with dual-attention mechanism. 217-229 - M. Jeya Sundari, N. C. Brintha:
Factorization-based active contour segmentation and pelican optimization-based modified bidirectional long short-term memory for ovarian tumor detection. 230-245 - Rajneesh Kumar Patel, Manish Kashyap:
Automated screening of glaucoma stages from retinal fundus images using BPS and LBP based GLCM features. 246-261 - Madan Parajuli, Mohamed Shaban, Thuy L. Phung:
Automated differentiation of skin melanocytes from keratinocytes in high-resolution histopathology images using a weakly-supervised deep-learning framework. 262-275 - Salwan Tajjour, Sonia Garg, Shyam Singh Chandel, Diksha Sharma:
A novel hybrid artificial neural network technique for the early skin cancer diagnosis using color space conversions of original images. 276-286 - Bakul Gohel, Manish Khare:
EEG/MEG source imaging in the absence of subject's brain MRI scan: Perspective on co-registration and MRI selection approach. 287-298 - Jun Wu, Yaxin Zhang, Zhitao Xiao, Fang Zhang, Lei Geng:
Automated segmentation of diabetic macular edema in OCT B-scan images based on RCU-Net. 299-311 - Wei Guo, Shijie Liu, Zhaoxuan Gong, Guodong Zhang, Xiran Jiang:
Cascaded networks for the embryo classification on microscopic images using the residual external-attention. 312-322 - Asieh Khosravanian, Mohammad Rahmanimanesh, Parviz Keshavarzi, Saeed Mozaffari:
Enhancing level set brain tumor segmentation using fuzzy shape prior information and deep learning. 323-339 - Gurinderjeet Kaur, Prashant Singh Rana, Vinay Arora:
Deep learning and machine learning-based early survival predictions of glioblastoma patients using pre-operative three-dimensional brain magnetic resonance imaging modalities. 340-361 - Shan Jin, Hongming Xu, Yue Dong, Xinyu Hao, Fengying Qin, Qi Xu, Yong Zhu, Fengyu Cong:
Automatic cervical cancer segmentation in multimodal magnetic resonance imaging using an EfficientNet encoder in UNet++ architecture. 362-377 - Sinan Altun, Ahmet Alkan:
LSS-net: 3-dimensional segmentation of the spinal canal for the diagnosis of lumbar spinal stenosis. 378-388 - Xiaoyan Fan, Zhanquan Sun, Engang Tian, Zhong Yin, Gaoyu Cao:
Medical image contrast enhancement based on improved sparrow search algorithm. 389-402 - Haotian Zhang, Ning Jia, Keqiang Zhuo, Weidong Zhao:
Retinal fundus image registration framework using Bayesian integration and asymmetric Gaussian mixture model. 403-418 - Diana Baby, Sujitha Juliet, M. M. Anishin Raj:
An efficient lymphocytic leukemia detection based on EfficientNets and ensemble voting classifier. 419-426 - N. Nithya, M. S. K. Manikandan:
Resolution improvement using enriched Krylov subspace for microwave tomography breast imaging system. 427-442
Volume 33, Number 2, March 2023
Research Articles
- Ali Abbasian Ardakani, Afshin Mohammadi, Fariborz Faeghi, U. Rajendra Acharya:
Performance evaluation of 67 denoising filters in ultrasound images: A systematic comparison analysis. 445-464 - Syed Sajid Hussain, Jainy Sachdeva, Chirag Kamal Ahuja, Abhiav Singh:
Enc-Unet: A novel method for Glioma segmentation. 465-482 - Anjan Gudigar, U. Raghavendra, Tejaswi N. Rao, Jyothi Samanth, Venkatesan Rajinikanth, Suresh Chandra Satapathy, Edward J. Ciaccio, Wai-Yee Chan, U. Rajendra Acharya:
FFCAEs: An efficient feature fusion framework using cascaded autoencoders for the identification of gliomas. 483-494 - Pallabi Sharma, Dipankar Das, Anmol Gautam, Bunil Kumar Balabantaray:
LPNet: A lightweight CNN with discrete wavelet pooling strategies for colon polyps classification. 495-510 - Angamuthu Rajasekaran Kavitha, Karthikeyan Palaniappan:
Brain tumor segmentation using a deep Shuffled-YOLO network. 511-522 - Jing Wang, Wenjuan Zhang, Rui Zhu:
A multimodal molecular image fusion method based on relative total variation and co-saliency detection. 523-546 - Heewon Yoon, Yongwon Cho, Kyung-Sik Ahn, Hee-Gone Lee, Chang Ho Kang, Beom Jin Park:
Using a convolutional neural network model to derive imaging landmarks for lumbar spine numbering on axial magnetic resonance images. 547-555 - Ellák Somfai, Benjámin Baffy, Kristian Fenech, Rita Hosszú, Dorina Korózs, Marcell Pólik, Miklós Sárdy, András Lorincz:
Handling dataset dependence with model ensembles for skin lesion classification from dermoscopic and clinical images. 556-571 - Muhammad Attique Khan, Awais Khan, Majed Alhaisoni, Abdullah Alqahtani, Shtwai Alsubai, Meshal Alharbi, Nazir Ahmed Malik, Robertas Damasevicius:
Multimodal brain tumor detection and classification using deep saliency map and improved dragonfly optimization algorithm. 572-587 - Nitika Goenka, Shamik Tiwari:
Alzheimer's detection using various feature extraction approaches using a multimodal multi-class deep learning model. 588-609 - Latifa Houria, Noureddine Belkhamsa, Assia Cherfa, Yazid Cherfa:
Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines. 610-621 - Zhiwei Ye, Zilun Song, Pengfei Li, Mingwei Wang, Wenguang Hou:
A modified threshold score-based multilevel thresholding segmentation technique for brain magnetic resonance images using opposition-based learning hybrid rice optimization algorithm. 622-643 - K. H. Vijaya Kumari, Soubhagya Sankar Barpanda:
Residual UNet with Dual Attention - An ensemble residual UNet with dual attention for multi-modal and multi-class brain MRI segmentation. 644-658 - Shobha Jose, Thomas George Selvaraj, Kenneth Samuel, Jobin T. Philip, Sairamya Nanjappan Jothiraj, Subathra Muthu Swamy Pandian, Vikram Shenoy Handiru, Suviseshamuthu Easter Selvan:
Intramuscular EMG classifier for detecting myopathy and neuropathy. 659-669 - Ming Meng, Zhichao Dong, Yunyuan Gao, Qingshan She:
Optimal channel and frequency band-based feature selection for motor imagery electroencephalogram classification. 670-679 - Ramzi Mahmoudi:
Ventricular segmentation and modeling using topological watershed transformation and harmonic state model. 680-700 - Geetha Sushama, Gopakumar Chandrasekhara Menon:
Attention augmented residual autoencoder for efficient polyp segmentation. 701-713 - Rupesh Mahamune, Shahedul Haque Laskar:
An automatic channel selection method based on the standard deviation of wavelet coefficients for motor imagery based brain-computer interfacing. 714-728 - P. Vaidehi Nayantara, Surekha Kamath, Manjunath Kanabagatte Nanjundappa, Rajagopal Kadavigere:
Automatic liver tumor segmentation on multiphase computed tomography volume using SegNet deep neural network and K-means clustering. 729-745 - Balakumaresan Ragupathy, Bharath Subramani, Selvapandian Arumugam:
A novel approach for MR brain tumor classification and detection using optimal CNN-SVM model. 746-759 - Muhammet Serdar Bugday, Mehmet Akcicek, Harun Bingol, Muhammed Yildirim:
Automatic diagnosis of ureteral stone and degree of hydronephrosis with proposed convolutional neural network, Relief, and gradient-weighted class activation mapping based deep hybrid model. 760-769
Corrigendum
- Correction to 'ORYX-MRSI: A fully-automated open-source software for proton magnetic resonance spectroscopic imaging data analysis'. 770
Volume 33, Number 3, May 2023
Editorial
- Mohamed L. Seghier:
Using ChatGPT and other AI-assisted tools to improve manuscripts readability and language. 773-775
Research Articles
- Mahmut Agrali, Volkan Kilic, Aytug Onan, Esra Meltem Koç, Ali Murat Koc, Rasit Eren Büyüktoka, Türker Acar, Zehra Adibelli:
DeepChestNet: Artificial intelligence approach for COVID-19 detection on computed tomography images. 776-788 - S. Jeevitha, K. Valarmathi:
A joint segmentation and classification framework for COVID-19 infection segmentation and detection from chest CT images. 789-806 - Rajneesh Kumar Patel, Manish Kashyap:
Automated diagnosis of COVID stages using texture-based Gabor features in variational mode decomposition from CT images. 807-821 - Sangeetha Balachandran, Vidhyapriya Ranganathan:
Semantic context-aware attention UNET for lung cancer segmentation and classification. 822-836 - Quan Sheng, Yutao Zhang, Haifeng Shi, Zhuqing Jiao:
Global iterative optimization framework for predicting cognitive function statuses of patients with end-stage renal disease. 837-852 - Muhammet Emin Sahin:
Image processing and machine learning-based bone fracture detection and classification using X-ray images. 853-865 - Dania Mushtaq, Tahir Mustafa Madni, Uzair Iqbal Janjua, Fozia Anwar, Ahmad Kakakhail:
An automatic gastric polyp detection technique using deep learning. 866-880 - Swati P. Pawar, Sanjay N. Talbar:
Multi-level deep learning based lung cancer classifier for classification based on tumour-node-metastasis approach. 881-894 - Necip Cinar, Mehmet Kaya, Buket Kaya:
A novel convolutional neural network-based approach for brain tumor classification using magnetic resonance images. 895-908 - Geetha Pavani Pappu, Sreekar Tankala, Krishna Talabhaktula, Birendra Biswal:
EANet: Multiscale autoencoder based edge attention network for fluid segmentation from SD-OCT images. 909-927 - Prasenjit Dhar, Suganya Devi Kothandapani, Satish Kumar Satti, Srinivasan Padmanabhan:
HPKNN: Hyper-parameter optimized KNN classifier for classification of poikilocytosis. 928-950 - Mehmet Ercan Nergiz:
Federated learning-based colorectal cancer classification by convolutional neural networks and general visual representation learning. 951-964 - Priyanka Arora, Parminder Singh, Akshay Girdhar, Rajesh Vijayvergiya:
Performance analysis of various denoising filters on intravascular ultrasound coronary artery images. 965-984 - Sandeep Singh Sengar, Christopher Meulengracht, Mikael Ploug Boesen, Anders Føhrby Overgaard, Henrik Gudbergsen, Janus Damm Nybing, Mathias Perslev, Erik Bjørnager Dam:
Multi-planar 3D knee MRI segmentation via UNet inspired architectures. 985-998 - Mustafa Gürman, Bülent Bilgehan, Özlem Sabuncu, Omid Mirzaei:
A powerful probabilistic model for noise analysis in medical images. 999-1013 - Mahesh Gour, Sweta Jain, Sushant Kaushal:
XCapsNet: A deep neural network for automated detection of diabetic retinopathy. 1014-1027 - Deepak Saini, Ashima Khosla, Trilok Chand, Devendra K. Chouhan, Mahesh Prakash:
Automated knee osteoarthritis severity classification using three-stage preprocessing method and VGG16 architecture. 1028-1047 - Ravindranath Kadirappa, Deivalakshmi Subbian, Pandeeswari Ramasamy, Seok-Bum Ko:
Histopathological carcinoma classification using parallel, cross-concatenated and grouped convolutions deep neural network. 1048-1061 - Yanyan Shi, Zhang Yuhang, Meng Wang, Gao Zhen, Dai Meng, Feng Fu:
Image reconstruction based on a modified bird swarm optimization algorithm for electrical impedance tomography. 1062-1072 - Prabhishek Singh, Manoj Diwakar:
Total variation-based ultrasound image despeckling using method noise thresholding in non-subsampled contourlet transform. 1073-1091 - Qi Ding, Navid Razmjooy:
An optimal diagnosis system for melanoma dermoscopy images based on enhanced design of horse herd optimizer. 1092-1107
Volume 33, Number 4, July 2023
Editorial
- Yudong Zhang:
Fighting against COVID-19: Innovations and applications. 1111-1115
Research Articles
- Abdullahi Umar Ibrahim, Ayse Gunnay Kibarer, Fadi Al-Turjman, Serife Kaba:
Large-scaled detection of COVID-19 from X-ray using transfer learning. 1116-1128 - Upendra Kumar Acharya, Mohammad Taha Ali, Mohd Kaif Ahmed, Mohd Tabish Siddiqui, Harsh Gupta, Sandeep Kumar, Ajey Shakti Mishra:
Hybrid deep neural network for automatic detection of COVID-19 using chest x-ray images. 1129-1143 - Kenan Erdem, Mehmet Ali Kobat, Mehmet Nail Bilen, Yunus Balik, Sevim Alkan, Feyzanur Cavlak, Ahmet Kursad Poyraz, Prabal Datta Barua, Ilknur Tuncer, Sengul Dogan, Mehmet Baygin, Mehmet Erten, Turker Tuncer, Ru San Tan, U. Rajendra Acharya:
Hybrid-Patch-Alex: A new patch division and deep feature extraction-based image classification model to detect COVID-19, heart failure, and other lung conditions using medical images. 1144-1159 - Faraz Bagwan, Nitin Pise:
A precise and timely graph-based approach to identify SARS Covid19 infection from medical imaging data using IsoCovNet. 1160-1176 - Leena Samantaray, Rutuparna Panda, Manoj Kumar Naik, Ajith Abraham:
A novel adaptive class weight adjustment-based multiclass segmentation error minimization technique for COVID-19 X-ray image analysis. 1177-1193 - Dolly Das, Saroj Kumar Biswas, Sivaji Bandyopadhyay:
Mixed attention and regularized COVID-19 network: An approach to detection of COVID-19 with chest x-ray images. 1194-1222 - Haytham Al Ewaidat, Sara Balawi, Ziad Bataineh, Ahmed Al-Dwairi, Majd Al-Khalily, Khalaf Abdel Azez, Ali Almakhadmeh:
Establishment of national diagnostic reference levels as guidelines for computed tomography radiation in Jordan. 1223-1234 - Nan Yan, Ye Tao:
Pneumonia X-ray detection with anchor-free detection framework and data augmentation. 1235-1246 - Muhammad Tahir Akram, Sohail Asghar, Ahmad Raza Shahid:
Effective data augmentation for brain tumor segmentation. 1247-1260 - Jingyu Zhu, Jianming Ye, Leshui Dong, Xiaofei Ma, Na Tang, Peng Xu, Wei Jin, Ruipeng Li, Guang Yang, Xiaobo Lai:
Non-invasive prediction of overall survival time for glioblastoma multiforme patients based on multimodal MRI radiomics. 1261-1274 - Muhammad Attique Khan, Tallha Akram, Yudong Zhang, Majed Alhaisoni, Abdullah Al Hejaili, Khalid Adel Shaban, Usman Tariq, Muhammad H. Zayyan:
SkinNet-ENDO: Multiclass skin lesion recognition using deep neural network and Entropy-Normal distribution optimization algorithm with ELM. 1275-1292 - Erkan Duman, Zafer Tolan:
Ensemble the recent architectures of deep convolutional networks for skin diseases diagnosis. 1293-1305 - Sreedhar Kollem, Katta Rama Linga Reddy, Chintha Rajendra Prasad, Avishek Chakraborty, J. Ajayan, S. Sreejith, Sandip Bhattacharya, L. Maria Irudaya Leo Joseph, Ravichander Janapati:
AlexNet-NDTL: Classification of MRI brain tumor images using modified AlexNet with deep transfer learning and Lipschitz-based data augmentation. 1306-1322 - V. Nehru, V. Prabhu:
Segmentation of brain tumor subregions with depthwise separable dense U-NET (DSDU-NET). 1323-1334 - Buket Kaya, Muhammed Önal:
A CNN transfer learning-based approach for segmentation and classification of brain stroke from noncontrast CT images. 1335-1352 - Anisha Isaac, H. Khanna Nehemiah, Snofy D. Dunston, Arputharaj Kannan:
Feature selection and classification using bio-inspired algorithms for the diagnosis of pulmonary emphysema subtypes. 1353-1367 - Murat Aydogan:
A hybrid deep neural network-based automated diagnosis system using x-ray images and clinical findings. 1368-1382 - Khalil Barbouchi, Dhekra El Hamdi, Ines Elouedi, Takwa Ben Aïcha, Afef Kacem Echi, Ihsen Slim:
A transformer-based deep neural network for detection and classification of lung cancer via PET/CT images. 1383-1395 - Colline Blanc, Shiva Shahrampour, Feroze B. Mohamed, Benjamin De Leener:
Combining PropSeg and a convolutional neural network for automatic spinal cord segmentation in pediatric populations and patients with spinal cord injury. 1396-1405 - Manvir Kaur, Rahul Upadhyay, Vinay Kumar:
E-CNNet: Time-reassigned Multisynchrosqueezing transform-based deep learning framework for MI-BCI task classification. 1406-1423 - Xiaoying Pan, Jia Lian, Qiqi He, Yufeng Xue, Hao Wang, Dalin He:
Real-time coloring method of laser surgery video based on generative adversarial network. 1424-1436 - Mohammad R. Salmanpour, Mahdi Hosseinzadeh, Mahya Bakhtiyari, Mehdi Maghsudi, Arman Rahmim:
Prediction of drug amount in Parkinson's disease using hybrid machine learning systems and radiomics features. 1437-1449 - Chun-Ling Lin, Zhi-Xiang Jiang:
Development of preprocessing methods and revised EfficientNet for diabetic retinopathy detection. 1450-1466
Volume 33, Number 5, September 2023
Research Articles
- Tong Wang, Fubin Wu, Haoran Lu, Shengzhou Xu:
CA-UNet: Convolution and attention fusion for lung nodule segmentation. 1469-1479 - Hakan Özcan:
BUS-CAD: A computer-aided diagnosis system for breast tumor classification in ultrasound images using grid-search-optimized machine learning algorithms with extended and Boruta-selected features. 1480-1493 - Seenia Francis, P. B. Jayaraj, P. N. Pournami, Niyas Puzhakkal:
ContourGAN: Auto-contouring of organs at risk in abdomen computed tomography images using generative adversarial network. 1494-1504 - Majid Vafaeezadeh, Hamid Behnam, Ali Hosseinsabet, Parisa Gifani:
CarpNet: Transformer for mitral valve disease classification in echocardiographic videos. 1505-1514 - Jian Chen, Jiaze Wan, Zhenghan Fang, Lifang Wei:
LMSA-Net: A lightweight multi-scale aware network for retinal vessel segmentation. 1515-1530 - Shen Jiang, Jinjiang Li, Zhen Hua:
GR-Net: Gated axial attention ResNest network for polyp segmentation. 1531-1548 - Yonggong Ren, Wenqiang Xu, Yuanxin Mao, Yuechu Wu, Bo Fu, Dang N. H. Thanh:
Few-shot learning for dermatological conditions with Lesion Area Aware Swin Transformer. 1549-1560 - Hui Wang, Qianqian Qi, Weijia Sun, Xue Li, Boxin Dong, Chunli Yao:
Classification of skin lesions with generative adversarial networks and improved MobileNetV2. 1561-1576 - Shweta Tyagi, Sanjay N. Talbar:
Predicting lung cancer treatment response from CT images using deep learning. 1577-1592 - Wadhah Ayadi, Wajdi Elhamzi, Mohamed Atri:
A deep conventional neural network model for glioma tumor segmentation. 1593-1605 - J. Sofia Bobby, B. Suresh Chander Kapali, Ushus S. Kumar, M. A. Femina:
QCBO-WSVM: Quantum chaos butterfly optimization-based weighted support vector machine for neuropathic pain detection from EEG signal. 1606-1620 - Maria Naz, Munam Ali Shah, Hasan Ali Khattak, Abdul Wahid, Muhammad Nabeel Asghar, Hafiz Tayyab Rauf, Muhammad Attique Khan, Zoobia Ameer:
Multi-branch sustainable convolutional neural network for disease classification. 1621-1633 - Mengdan Cheng, Juan Qin, Lianrong Lv, Biao Wang, Lei Li, Dan Xia, Shike Wang:
A dual channel and spatial attention network for automatic spine segmentation of MRI images. 1634-1646 - Razieh Ganjee, Mohsen Ebrahimi Moghaddam, Ramin Nourinia:
A generalizable approach based on the U-Net model for automatic intraretinal cyst segmentation in SD-OCT images. 1647-1660 - Esraa Asem Shaker, Ahmed S. El-Hossiny, Ahmed Hisham Kandil, Ahmed M. El-Bialy, Heba M. Afify:
Advanced imaging system for brain tumor automatic classification from MRI images using HOG and BOF feature extraction approaches. 1661-1671 - Sheng-Sheng Wang, Zihao Fu, Bilin Wang, Yulong Hu:
Fusing feature and output space for unsupervised domain adaptation on medical image segmentation. 1672-1681 - V. Swetha, G. Vadivu:
Classifications of benign and malignant mammogram images using Gabor-modified CNN architecture. 1682-1695 - Shankar Thawkar, Vijay Katta, Ajay Raj Parashar, Law Kumar Singh, Munish Khanna:
Breast cancer: A hybrid method for feature selection and classification in digital mammography. 1696-1712 - Zuzheng Chang, Dragan Rodriguez:
Optimized lung cancer detection by amended whale optimizer and rough set theory. 1713-1726 - Evgin Göçeri:
Comparison of the impacts of dermoscopy image augmentation methods on skin cancer classification and a new augmentation method with wavelet packets. 1727-1744 - Sahar Gull, Shahzad Akbar, Syed Muhammad Naqi:
A deep learning approach for multi-stage classification of brain tumor through magnetic resonance images. 1745-1766 - Jiffy Joseph, Aparajit Singh, Pournami Pulinthanathu Narayanan, Jayaraj Pottekkattuvalappil Balakrishnan, Niyas Puzhakkal:
Cone beam computed tomography enhancement using feature-embedded variational autoencoder with a perceptual loss function. 1767-1778 - Gulhan Kilicarslan, Canan Koc, Fatih Özyurt, Yeliz Gul:
Breast lesion classification using features fusion and selection of ensemble ResNet method. 1779-1795 - Shree Prakash, Jagadeesh Kakarla:
Three level automatic segmentation of optic disc using LAB color space contours and morphological operation. 1796-1813 - Devanand Ongole, S. Saravanan:
Colour-based segmentation using FCM and K-means clustering for 3D thyroid gland state image classification using deep convolutional neural network structure. 1814-1826
Volume 33, Number 6, November 2023
Research Articles
- Om Ramakisan Varma, Mala Kalra, Sheeraz Kirmani:
COVID-19: A systematic review of prediction and classification techniques. 1829-1857 - Lotfi Mhamdi, Oussama Dammak, François Cottin, Imed Ben Dhaou:
Deep learning for COVID-19 contamination analysis and prediction using ECG images on Raspberry Pi 4. 1858-1869 - Yuan Yang, Lin Zhang, Lei Ren, Xiaohan Wang:
Distributed autoencoder classifier network for small-scale and scattered COVID-19 dataset classification. 1870-1881 - Aya Nader Salama, M. A. Mohamed, Hanan M. Amer, Mohamed Maher Ata:
An efficient quantification of COVID-19 in chest CT images with improved semantic segmentation using U-Net deep structure. 1882-1901 - Yoshio Watanabe, Akira Taniguchi, Hidekazu Tomimoto:
Maximum isotope accumulation in the retrosplenial cortex during amnesia attack and its temporal change suggest cortical spreading depression as a pathophysiology of patients with transient global amnesia. 1902-1913 - Xue Xia, Kun Zhan, Yuming Fang, Wenhui Jiang, Fei Shen:
Lesion-aware network for diabetic retinopathy diagnosis. 1914-1928 - Ömer Faruk Söylemez:
Forward selection-based ensemble of deep neural networks for melanoma classification in dermoscopy images. 1929-1943 - Xinying Wang, Jian Yi, Yang Li:
Cerebral stroke classification based on fusion model of 3D EmbedConvNext and 3D Bi-LSTM network. 1944-1956 - G. Renith, A. Senthilselvi:
An efficient skin cancer detection and classification using Improved Adaboost Aphid-Ant Mutualism model. 1957-1972 - Zhi Wang, Feng Gao, Long Yu, Shengwei Tian:
UACENet: Uncertain area attention and cross-image context extraction network for polyp segmentation. 1973-1987 - N. Alizadeh, Sajjad Afrakhteh, Mohammad Reza Mosavi:
Deep CNN-based classification of motor imagery tasks from EEG signals using 2D wavelet transformed images of adaptively reconstructed signals from MVMD decomposed modes. 1988-2011 - Xiaochen Wang, Yanhui Ding, Yuanjie Zheng:
Multiscale attention network for retinal vein occlusion classification with multicolor image. 2012-2022 - Zhenrun Zhan, Pengyong Han, Xu Tang, Jinpeng Yang, Xiaodan Bi, Tingting Zhao:
Applying machine learning to screen for acute myocardial infarction-related biomarkers and immune infiltration features and validate it clinically and experimentally. 2023-2043 - Ciyamala Kushbu Sadhanandan, Inbamalar Tharcis Mariapushpam, Sudha Suresh:
An efficient deep learning algorithm for the segmentation of cardiac ventricles. 2044-2060 - Kamakshi Rautela, Dinesh Kumar, Vijay Kumar:
Active contour and texture features hybrid model for breast cancer detection from ultrasonic images. 2061-2072 - Qiang Li, Hengxin Liu, Weizhi Nie, Ting Wu:
Brain tumor image segmentation based on prior knowledge via transformer. 2073-2087 - Yuanyuan Peng, Jiaxing Zhang:
Lung lobe segmentation in computed tomography images based on multi-feature fusion and ensemble learning framework. 2088-2099 - Priyanka Grover, Hari Shankar Singh, Sanjaya Kumar Sahu:
Design and analysis of a super compact UWB antenna for accurate detection of breast tumors using monostatic radar-based microwave imaging technique. 2100-2117 - Qiong Liu, Yue Li, Zi-Xin Zhai, Hai-Yan Jia, Li-Ping Liu:
An improved method for thyroid nodule ultrasound image segmentation based on U2-Net. 2118-2127 - A. Pandian, Udhayakumar Ganesan:
Improved multiple sclerosis diagnosis with advanced deep learning techniques. 2128-2141 - Rajeshwari Rengarajan, M. S. Geetha Devasena, G. Gopu:
Enhanced grasshopper optimization-based selection of ultrasound and elastography features for breast lesion classification. 2142-2156 - Narayanan Krishnasamy, Thangaraj Ponnusamy:
Deep learning-based robust hybrid approaches for brain tumor classification in magnetic resonance images. 2157-2177 - Hamed Zamanian, Ahmad Shalbaf:
Grading of steatosis, fibrosis, lobular inflammation, and ballooning from liver pathology images using pre-trained convolutional neural networks. 2178-2193 - Shaobo Wang, Zaoqin Chen, Yangyang Liu, Yubing Liu, Zhiyu Qian, Lin Meng:
Relationship between reduced scattering coefficient and intracranial pressure in clinical patients under different brain edema states. 2194-2202 - Malvika Ashok, Abhishek Gupta, Mohit Pandey:
HCIU: Hybrid clustered inception-based UNET for the automatic segmentation of organs at risk in thoracic computed tomography images. 2203-2217
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