Abstract: Early and accurate detection of deadly diseases from medical images is a major challenge in healthcare. In this paper, we develop a new methodology to solve this problem by using "Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs) and Explainable Artificial Intelligence (XALI)" techniques. Using GANs to generate high quality synthetic medi- cal images extends the limited training data baseline, allowing the CNN model to improve performance. Next we designed our CNN model layered with multiple convolutional and pooling layers that can classifies the images into different disease categories such as brain tumor. "XAI techniques, such as Grad-CAM is incorporated to increase the understandable and transparency of the CNN model's decision.......
Keywords — Convolutional Neural Networks (CNNs), Med- ical Image Analysis, Disease Detection, Explainable Artificial Intelligence (XAI), Grad-CAM, GANs.
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