Version 1
: Received: 21 October 2024 / Approved: 21 October 2024 / Online: 22 October 2024 (11:57:43 CEST)
How to cite:
Khalkhali, V.; Azim, S. M.; Dehzangi, I. ExShall-CNN: An Explainable Shallow Convolutional Neural Network for Medical Image Segmentation. Preprints2024, 2024101667. https://doi.org/10.20944/preprints202410.1667.v1
Khalkhali, V.; Azim, S. M.; Dehzangi, I. ExShall-CNN: An Explainable Shallow Convolutional Neural Network for Medical Image Segmentation. Preprints 2024, 2024101667. https://doi.org/10.20944/preprints202410.1667.v1
Khalkhali, V.; Azim, S. M.; Dehzangi, I. ExShall-CNN: An Explainable Shallow Convolutional Neural Network for Medical Image Segmentation. Preprints2024, 2024101667. https://doi.org/10.20944/preprints202410.1667.v1
APA Style
Khalkhali, V., Azim, S. M., & Dehzangi, I. (2024). ExShall-CNN: An Explainable Shallow Convolutional Neural Network for Medical Image Segmentation. Preprints. https://doi.org/10.20944/preprints202410.1667.v1
Chicago/Turabian Style
Khalkhali, V., Sayed Mehedi Azim and Iman Dehzangi. 2024 "ExShall-CNN: An Explainable Shallow Convolutional Neural Network for Medical Image Segmentation" Preprints. https://doi.org/10.20944/preprints202410.1667.v1
Abstract
Explainability is essential for AI models, especially in clinical settings where understanding the model's decisions is crucial. Despite their impressive performance, black-box AI models are unsuitable for clinical use if their operations cannot be explained to clinicians. While deep neural networks (DNNs) represent the forefront of model performance, their explanations are often not easily interpretable by humans. On the other hand, using hand-crafted features extracted to represent different aspects of the input data and traditional machine learning models are generally more understandable. However, they often lack the effectiveness of advanced models due to human limitations in feature design. To address this, we propose ExShall-CNN, a novel explainable shallow convolutional neural network for medical image processing. This model enhances hand-crafted features to maintain human interpretability while achieving performance levels comparable to advanced deep convolutional networks, such as U-Net, for medical image segmentation. ExShall-CNN and its source code are publicly available at: https://github.com/MLBC-lab/ExShall-CNN
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.
