Version 1
: Received: 28 May 2024 / Approved: 28 May 2024 / Online: 28 May 2024 (12:46:59 CEST)
How to cite:
Guha, S.; Kodipalli, A.; Fernandes, S. L.; Dasar, S. Explainable AI for Interpretation of Ovarian Tumor Classification Using Custom ResNet60. Preprints2024, 2024051865. https://doi.org/10.20944/preprints202405.1865.v1
Guha, S.; Kodipalli, A.; Fernandes, S. L.; Dasar, S. Explainable AI for Interpretation of Ovarian Tumor Classification Using Custom ResNet60. Preprints 2024, 2024051865. https://doi.org/10.20944/preprints202405.1865.v1
Guha, S.; Kodipalli, A.; Fernandes, S. L.; Dasar, S. Explainable AI for Interpretation of Ovarian Tumor Classification Using Custom ResNet60. Preprints2024, 2024051865. https://doi.org/10.20944/preprints202405.1865.v1
APA Style
Guha, S., Kodipalli, A., Fernandes, S. L., & Dasar, S. (2024). Explainable AI for Interpretation of Ovarian Tumor Classification Using Custom ResNet60. Preprints. https://doi.org/10.20944/preprints202405.1865.v1
Chicago/Turabian Style
Guha, S., Steven L. Fernandes and Santosh Dasar. 2024 "Explainable AI for Interpretation of Ovarian Tumor Classification Using Custom ResNet60" Preprints. https://doi.org/10.20944/preprints202405.1865.v1
Abstract
Deep learning architectures like ResNet and Inception have produced accurate predictions for classifying benign and malignant tumors in the healthcare domain. This enables healthcare institutions to make data-driven decisions and potentially enable early detection of malignancy by employing computer-vision-based deep learning algorithms. These Convolutional Neural Network algorithms, in addition to requiring huge amounts of data, can identify higher and lower-level features that are significant while classifying tumors into benign or malignant. However, these algorithms have limitations concerning explainability and identifying the exact features that are of importance and contributing to the final classification of tumors as benign or malignant. In this paper, we implement several explainable AI techniques, namely, LIME, Saliency Map, Occlusion Analysis, Grad-CAM, SHAP, and Smooth Grad to interpret the results of a custom ResNet60 classifier in the classification of ovarian tumors as benign or malignant. The ResNet60 model attained an accuracy of 97.50% for the test dataset.
Keywords
ResNet60; Explainable AI methods; LIME, Saliency Map; Occlusion Analysis; GRAD-CAM; SHAP; SmoothGrad; Interpretable AI
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
