DeepMediX: A Deep Learning-Driven Resource-Efficient Medical Diagnosis
  Across the Spectrum
Kishore Babu Nampalle Pradeep Singh Uppala Vivek Narayan Balasubramanian Raman
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.