Innovative Deep Learning Models for Accurate Segmentation and Classification in Oncological Diagnosis Data
Archana R
Department of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur-603 203, Chengalpattu, Tamil Nadu, India.
Department of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur-603 203, Chengalpattu, Tamil Nadu, India.
Accurate identification and classification of tumours are essential for effectively diagnosing and treating hepatocellular carcinoma and metastatic disease. However, the heterogeneous nature of tumours, characterized by irregular boundaries and variations in shape, size, and location, poses significant challenges for precise and automated segmentation and classification. With recent advances in artificial intelligence, deep learning has emerged as a powerful tool for medical image analysis. Although current clinical methods offer baseline performance in tumour classification, there is still considerable scope for improving diagnostic accuracy. This research proposes an innovative deep-learning framework to enhance the segmentation and classification of tumours. The approach begins by enhancing image contrast using histogram equalization and reducing noise via a median filter, regions are then accurately segmented from abdominal CT images using Mask R-CNN, a state-of-the-art model based on region-based convolutional neural networks. The segmented outputs are further processed using an Enhanced Swin Transformer to mitigate overfitting and boost classification performance. Experimental results demonstrate that the proposed model achieves superior accuracy and robustness across diverse CT image datasets, exhibiting strong performance even in noise.
Keywords
Segmentation, Deep Learning, RCNN Classification, CT Image, Mask R-CNN.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Archana R and Anand L;
Methodology: Anand L;
Visualization: Archana R;
Investigation: Archana R and Anand L;
Supervision: Anand L;
Validation: Archana R;
Writing- Reviewing and Editing: Archana R and Anand L;
All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to Dr. Anand L for this research completion and support.
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Anand L
Department of Networking and Communications, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur-603 203, Chengalpattu, Tamil Nadu, India.
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Cite this article
Archana R and Anand L, “Innovative Deep Learning Models for Accurate Segmentation and Classification in Oncological Diagnosis Data”, Journal of Machine and Computing, vol.5, no.3, pp. 1417-1426, July 2025, doi: 10.53759/7669/jmc202505112.
