Medical Image Analysis using Distributed Deep Learning Models

Medical image analysis plays a crucial role in modern medicine, serving as an indispensable tool for clinicians to enhance diagnostic precision and establish personalized treatment programs. Our team has devised an innovative method for medical image analysis, employing distributed deep learning techniques for both COVID-19 and brain tumor datasets. Traditional approaches to medical image analysis often demand significant computational resources and extensive data, rendering their implementation challenging in real-world scenarios. In order to overcome these obstacles, we propose a distributed deep learning framework that enables parallel processing of medical images across multiple nodes. Our framework leverages convolutional neural networks and transfer learning to achieve exceptional accuracy in the detection of COVID-19 and brain tumors from inputted images, respectively. Through the evaluation of an extensive dataset of medical images, we have successfully demonstrated the efficiency of our approach in accurately detecting these conditions