Modified Transformer-Based Pixel Segmentation for Breast Tumor Detection Kamakshi Rautela, Dinesh Kumar, Vijay Kumar International Journal of Imaging Systems and Technology, 2025 This study introduces a novel hybrid deep learning model that combines residual convolutional networks and a multilayer perceptron (MLP)‐based transformer for precise breast lesion segmentation and classification using mammogram images. Initially, mammograms undergo preprocessing involving thresholding and Gabor‐based pixel segmentation to extract informative patches. The proposed model leverages deep features extracted via convolutional neural networks, which are subsequently processed through self‐attention and cross‐attention mechanisms in a modified transformer architecture to capture both local and global dependencies for classification. The approach is rigorously evaluated on the publicly available INbreast dataset, achieving classification accuracies of 98.17% for a three‐class (normal, benign, malignant) scenario and 96.74% for a more detailed five‐class classification. The model demonstrates strong capabilities in differentiating subtle variations between malignant and benign tissues. These promising results suggest significant potential for practical clinical implementation, assisting radiologists by providing highly accurate diagnostic insights. Notably, this approach contributes substantially to automated breast cancer diagnostics, highlighting the efficacy of integrating convolutional neural network features with transformer architectures for improved segmentation and classification outcomes.
Improved GAN for image resolution enhancement using ViT for breast cancer detection Kamakshi Rautela, Dinesh Kumar, Vijay Kumar International Journal of Imaging Systems and Technology, 2024 Breast cancer has a significant mortality rate and is widespread among women. Detecting it early is crucial for effective treatment. While mammograms provide detailed anatomical visuals, their accuracy is hindered by low sensitivity and high background noise, especially in dense breast tissue. This study introduces a new network architecture integrating generative adversarial networks (GANs) and vision transformers to improve reference‐based super‐resolution. The proposed model combines image generation and classification in a unified framework, eliminating the need for a separate classifier during training. To enhance GAN robustness, a unique two‐channel approach is employed, and transformer and residual learning techniques improve overall efficiency. The study introduces a novel synthetic image‐based model to improve feature detection for breast cancer classification. The proposed model shows the ability to understand information variability across different representations, enhancing accuracy and reducing computational time. Using the INbreast dataset, the results indicate the model's capability to generate high‐quality images with sensitivity and accuracy values of 0.932 and 0.988, respectively.
Active contour and texture features hybrid model for breast cancer detection from ultrasonic images Kamakshi Rautela, Dinesh Kumar, Vijay Kumar International Journal of Imaging Systems and Technology, 2023 Breast ultrasound is commonly used in the early detection of breast cancer. The existing geodesic‐based methods use pre‐defined filters that necessitate extensive prior knowledge to achieve the region of interest in input image. Furthermore, the majority of ultrasound images suffer from noise and acoustic shadowing, which reduces the accuracy of tumor detection. To make the breast ultrasound image more informative, the discriminative features can also be extracted to improve detection accuracy. This article proposes a method to combine Active Contour and Texture Feature Vectors to find discriminative patterns. A comprehensive set of discriminative features for cancer detection in ultrasound images is created by combining the two learning models. The Breast Ultrasound Images dataset is used to evaluate the suggested method and compare it to recently created algorithms. Experimental results reveal that the proposed approach outperforms the existing algorithms in terms of accuracy, recall, precision, Jaccard index, and F1 score.
ECG Signal Spectral Estimation and Noise Filtering Abhijit Singh Bhakuni, Kamakshi Rautela, Pradeep Juneja Proceedings IEEE 2018 International Conference on Advances in Computing Communication Control and Networking Icacccn 2018, 2018
RECENT SCHOLAR PUBLICATIONS
Artificial Intelligence in Epidemic Modeling and Pandemic Preparedness: A Comprehensive Review A Sharma, A Kumar, S Dhanka, S Maini, K Rautela, S Kant, A Kaur, ... Archives of Computational Methods in Engineering, 1-31 , 2026 2026 Citations: 1
From Past Innovations to Future Prospects: Advanced Computational Techniques in Automated Obscenity Detection K Rautela, H Vardhan, A Juneja, V Kumar Cybernetics and Systems, 1-40 , 2026 2026
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A comprehensive review on computational techniques for breast cancer: past, present, and future K Rautela, D Kumar, V Kumar Multimedia Tools and Applications 83 (31), 76267-76300 , 2024 2024 Citations: 11
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Active contour and texture features hybrid model for breast cancer detection from ultrasonic images K Rautela, D Kumar, V Kumar International Journal of Imaging Systems and Technology 33 (6), 2061-2072 , 2023 2023 Citations: 6
Detection and localization of breast lesion with VGG19 optimized vision transformer K Rautela, D Kumar, V Kumar 2022 4th International Conference on Artificial Intelligence and Speech … , 2022 2022 Citations: 8
An interpretable network to thermal images for breast cancer detection K Rautela, D Kumar, V Kumar 2022 International Conference on Electrical, Computer, Communications and … , 2022 2022 Citations: 7
A Systematic Review on Breast Cancer Detection Using Deep Learning Techniques: K. Rautela et al. K Rautela, D Kumar, V Kumar Archives of Computational Methods in Engineering 29 (7), 4599-4629 , 2022 2022 Citations: 87
Dual-modality synthetic mammogram construction for breast lesion detection using U-DARTS K Rautela, D Kumar, V Kumar Biocybernetics and Biomedical Engineering 42 (3), 1041-1050 , 2022 2022 Citations: 7
Performance Analysis of Digital Modulation Schemes Over Fading Channels K Rautela, SK Sunori, AS Bhakuni, N Bisht, S Maurya, PK Juneja, R Alagh International Conference on Innovative Computing and Communications … , 2021 2021
Single Dark Channel Prior Generalization of Smoggy Image A Saini, A Sharma, K Rautela Proceedings of the Second International Conference on Information Management … , 2021 2021 Citations: 1
BER Performance of Gray-Coded PSK-Modulated and QAM-Modulated MIMO systems K Rautela, N Belwal 2019 Second International Conference on Advanced Computational and … , 2019 2019 Citations: 6
ECG signal spectral estimation and noise filtering AS Bhakuni, K Rautela, P Juneja 2018 International Conference on Advances in Computing, Communication … , 2018 2018 Citations: 3
Continuous time state space model of dc motor using kalman filter K Rautela, AS Bhakuni, S Sunori International Journal On Emerging Technologies, India: Department Of ECE , 2017 2017 Citations: 2
Continuous Time State Space Model of DC Motor using Kalman Filter ASBSS Kamakshi Rautela International Journal on Emerging Technologies 8 (1), 588-590 , 2017 2017
EEG Analysis of Brain Signals: A Review K Rautela, N Singh International Journal on Emerging Technologies 8 (1), 585-587 , 2017 2017 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
A Systematic Review on Breast Cancer Detection Using Deep Learning Techniques: K. Rautela et al. K Rautela, D Kumar, V Kumar Archives of Computational Methods in Engineering 29 (7), 4599-4629 , 2022 2022 Citations: 87
UDCT: lung Cancer detection and classification using U-net and DARTS for medical CT images A Gupta, A Kumar, K Rautela Multimedia Tools and Applications 84 (18), 19065-19085 , 2025 2025 Citations: 24
Literature survey on wireless sensor network N Singh, K Rautela Int J Eng Comput Sci 5 (8), 17544-17548 , 2016 2016 Citations: 13
A comprehensive review on computational techniques for breast cancer: past, present, and future K Rautela, D Kumar, V Kumar Multimedia Tools and Applications 83 (31), 76267-76300 , 2024 2024 Citations: 11
Obscenity detection transformer for detecting inappropriate contents from videos K Rautela, D Sharma, V Kumar, D Kumar Multimedia Tools and Applications 83 (4), 10799-10814 , 2024 2024 Citations: 11
Improved GAN for image resolution enhancement using ViT for breast cancer detection K Rautela, D Kumar, V Kumar International journal of imaging systems and technology 34 (2), e22998 , 2024 2024 Citations: 8
DVRGNet: an efficient network for extracting obscenity from multimedia content K Rautela, D Sharma, V Kumar, D Kumar Multimedia Tools and Applications 83 (10), 28807-28825 , 2024 2024 Citations: 8
Detection and localization of breast lesion with VGG19 optimized vision transformer K Rautela, D Kumar, V Kumar 2022 4th International Conference on Artificial Intelligence and Speech … , 2022 2022 Citations: 8
An interpretable network to thermal images for breast cancer detection K Rautela, D Kumar, V Kumar 2022 International Conference on Electrical, Computer, Communications and … , 2022 2022 Citations: 7
Dual-modality synthetic mammogram construction for breast lesion detection using U-DARTS K Rautela, D Kumar, V Kumar Biocybernetics and Biomedical Engineering 42 (3), 1041-1050 , 2022 2022 Citations: 7
Active contour and texture features hybrid model for breast cancer detection from ultrasonic images K Rautela, D Kumar, V Kumar International Journal of Imaging Systems and Technology 33 (6), 2061-2072 , 2023 2023 Citations: 6
BER Performance of Gray-Coded PSK-Modulated and QAM-Modulated MIMO systems K Rautela, N Belwal 2019 Second International Conference on Advanced Computational and … , 2019 2019 Citations: 6
ECG signal spectral estimation and noise filtering AS Bhakuni, K Rautela, P Juneja 2018 International Conference on Advances in Computing, Communication … , 2018 2018 Citations: 3
Continuous time state space model of dc motor using kalman filter K Rautela, AS Bhakuni, S Sunori International Journal On Emerging Technologies, India: Department Of ECE , 2017 2017 Citations: 2
Artificial Intelligence in Epidemic Modeling and Pandemic Preparedness: A Comprehensive Review A Sharma, A Kumar, S Dhanka, S Maini, K Rautela, S Kant, A Kaur, ... Archives of Computational Methods in Engineering, 1-31 , 2026 2026 Citations: 1
Single Dark Channel Prior Generalization of Smoggy Image A Saini, A Sharma, K Rautela Proceedings of the Second International Conference on Information Management … , 2021 2021 Citations: 1
EEG Analysis of Brain Signals: A Review K Rautela, N Singh International Journal on Emerging Technologies 8 (1), 585-587 , 2017 2017 Citations: 1
From Past Innovations to Future Prospects: Advanced Computational Techniques in Automated Obscenity Detection K Rautela, H Vardhan, A Juneja, V Kumar Cybernetics and Systems, 1-40 , 2026 2026
Modified Transformer‐Based Pixel Segmentation for Breast Tumor Detection K Rautela, D Kumar, V Kumar International Journal of Imaging Systems and Technology 35 (4), e70166 , 2025 2025
Performance Analysis of Digital Modulation Schemes Over Fading Channels K Rautela, SK Sunori, AS Bhakuni, N Bisht, S Maurya, PK Juneja, R Alagh International Conference on Innovative Computing and Communications … , 2021 2021
