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

arXiv: https://arxiv.org/abs/2307.00324 · HTML: https://mlsanity.com/paper/2307.00324/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 1 Jul 2023.

## Builds on (most cited first, 12 of 12)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)](https://mlsanity.com/paper/1711.11279/) (2017)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Understanding Deep Networks via Extremal Perturbations and Smooth Masks](https://mlsanity.com/paper/1910.08485/) (2019)
- [A deep learning system for differential diagnosis of skin diseases](https://mlsanity.com/paper/1909.05382/) (2019)
- [Grounding Visual Explanations](https://mlsanity.com/paper/1807.09685/) (2018)
- [CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images](https://mlsanity.com/paper/2004.04931/) (2020)
- [ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions](https://mlsanity.com/paper/2201.01249/) (2022)
- [Adversarial Inpainting of Medical Image Modalities](https://mlsanity.com/paper/1810.06621/) (2018)
- [Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems](https://mlsanity.com/paper/2011.13169/) (2020)
- [Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence](https://mlsanity.com/paper/2206.03247/) (2022)
- [Transcending Grids: Point Clouds and Surface Representations Powering Neurological Processing](https://mlsanity.com/paper/2305.15426/) (2023)

## Abstract (arXiv)

In the rapidly evolving landscape of medical imaging diagnostics, achieving high accuracy while preserving computational efficiency remains a formidable challenge. This work presents textttDeepMediX, 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.