Journals:

• Oza, U., Gohel, B., Kumar, P., & Oza, P. (2024). Presegmenter cascaded framework for mammogram mass segmentation. International Journal of Biomedical Imaging, 2024(1), Article 9422083. https://doi.org/10.1155/2024/9422083
• Oza, P., Oza, U., Oza, R., Sharma, P., Patel, S., Kumar, P., & Gohel, B. (2024).Digital mammography dataset for breast cancer diagnosis research (DMID) with breast mass segmentation analysis. Biomedical Engineering Letters, 14(2), 317–330. https://doi.org/10.1007/s13534-023-00339-y
• Oza, U., Gohel, B., & Kumar, P. (2025). Exploring end-to-end breast mass detection and segmentation frameworks using normal and abnormal mammograms.Biomedical Engineering: Applications, Basis and Communications, Article 2550051. https://doi.org/10.4015/S1016237225500516

Conference/ Book Chapters:

• Oza, U., Gohel, B., & Kumar, P. (2023). Evaluation of normalization algorithms for breast mammogram mass segmentation. In Proceedings of the International Conference on Machine Learning and Data Engineering (ICMLDE 2023) (pp.2508–2517). Procedia Computer Science, 235. https://doi.org/10.1016/j.procs.2024.04.236
• Oza, U., Oza, P., Gohel, B., & Kumar, P. (2024, March). ConvNet Model Evaluation for Binary and Multilabel Breast Mammogram Mass Segmentation. In1292024 11th International Conference on Signal Processing and Integrated Networks(SPIN) (pp. 187-192). IEEE. https://doi.org/10.1109/SPIN60856.2024.10511398
• Oza, U., Patel, S., & Kumar, P. (2021). Noveme-color space net for image classification. In Intelligent Information and Database Systems: 13th Asian Conference, ACIIDS 2021, Phuket, Thailand, April 7–10, 2021, Proceedings 13 (pp. 531-543). Springer International Publishing.
• Pipara, A., Oza, U., & Mandal, S. (2021). Underwater image color correction using ensemble colorization network. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 2011-2020).
• Oza, U., Pipara, A., Mandal, S., & Kumar, P. (2022, July). Automatic image colorization using ensemble of deep convolutional neural networks. In 2022 IEEE Region 10 Symposium (TENSYMP) (pp. 1-6). IEEE.
• Gandhi, H., Agrawal, K., Oza, U., & Kumar, P. (2022, November). Diabetic retinopathy classification using pixel-level lesion segmentation. In Futuristic Trends in Networks and Computing Technologies: Select Proceedings of Fourth International Conference on FTNCT 2021 (pp. 405-417). Singapore: Springer Nature Singapore.
• Khimani, M., Raj, S., Oza, U., & Kumar, P. (2022, November). Generative Adversarial Network for Colorization of Mammograms. In Futuristic Trends in Networks and Computing Technologies: Select Proceedings of Fourth International Conference on FTNCT 2021 (pp. 13-24). Singapore: Springer Nature Singapore.
• Aghera, M., Singh, K. V., Vaishnani, K., Oza, U., & Gohel, B. (2023, October). Segmentation of Nuclei in H&E-Stained Histological Images using Deep Learning Framework: A Perspective on Ensemble Approach and Nuclei Count. In 2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 462-467). IEEE.

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