DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis Across the Spectrum

TL;DR

DeepMediX is a resource-efficient deep learning model based on MobileNetV2 that accurately classifies medical images like brain MRI and skin cancer scans, suitable for deployment on handheld devices and enhanced by federated learning.

Contribution

This work introduces DeepMediX, a novel lightweight model that combines high diagnostic accuracy with computational efficiency and incorporates federated learning for privacy-preserving collaborative training.

Findings

Achieves high accuracy on brain MRI and skin cancer datasets.

Outperforms existing models in some diagnostic tasks.

Suitable for real-time deployment on handheld devices.

Abstract

In the rapidly evolving landscape of medical imaging diagnostics, achieving high accuracy while preserving computational efficiency remains a formidable challenge. This work presents \texttt{DeepMediX}, a groundbreaking, resource-efficient model that significantly addresses this challenge. Built on top of the MobileNetV2 architecture, DeepMediX excels in classifying brain MRI scans and skin cancer images, with superior performance demonstrated on both binary and multiclass skin cancer datasets. It provides a solution to labor-intensive manual processes, the need for large datasets, and complexities related to image properties. DeepMediX's design also includes the concept of Federated Learning, enabling a collaborative learning approach without compromising data privacy. This approach allows diverse healthcare institutions to benefit from shared learning experiences without the necessity of direct data access, enhancing the model's predictive power while preserving the privacy and integrity of sensitive patient data. Its low computational footprint makes DeepMediX suitable for deployment on handheld devices, offering potential for real-time diagnostic support. Through rigorous testing on standard datasets, including the ISIC2018 for dermatological research, DeepMediX demonstrates exceptional diagnostic capabilities, matching the performance of existing models on almost all tasks and even outperforming them in some cases. The findings of this study underline significant implications for the development and deployment of AI-based tools in medical imaging and their integration into point-of-care settings. The source code and models generated would be released at https://github.com/kishorebabun/DeepMediX.