IRO Journals
Journal of Information Technology and Digital World

MediFusionNet: A Hybrid CNN-Based Multi-Disease Diagnostic Web Platform

Open Access
Published: 26 September, 2026
Pages: 351-364
  • Rahul Anand T. , Rahul Anand T.
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
  • Prithiviraj R. , Prithiviraj R.
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
  • Mohamed Bisail M. , Mohamed Bisail M.
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
    Student, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
  • Sathya T. Sathya T.
    Assistant professor, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
    Assistant professor, Department of Artificial Intelligence and Data Science, NPR College of Engineering and Technology, Dindigul, India
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How to Cite

T., Rahul Anand, Prithiviraj R., Mohamed Bisail M., and Sathya T. 2026. “MediFusionNet: A Hybrid CNN-Based Multi-Disease Diagnostic Web Platform”. Journal of Information Technology and Digital World 8 (4): 351-64. https://doi.org/10.36548/jitdw.2026.4.005.
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Keywords

Medical Image Analysis
Deep Learning
Hybrid Convolutional Neural Network (CNN)
Feature Fusion
Federated Learning
Multi-Disease Diagnosis
Web-Based Clinical Decision Support

Abstract

Disease diagnosis using medical images has become an indispensable part of healthcare today. This necessitates the development of smart and effective models. This research work proposes a MediFusionNet, a hybrid web-based CNN diagnostic tool for multi-disease diagnosis based on feature fusion between VGG16 and MobileNetV2 to enhance the classification performance for different medical imaging techniques. The proposed framework diagnoses Brain Tumors, Breast Cancer, Chest Diseases, and Lung Abnormalities from Brain MRI, Breast Ultrasound, Chest X-ray, and Lung CT datasets respectively. We apply a preprocessing phase, which consists of images' resizing, normalization, and augmentation to make the model robust. Federated learning technique is applied to train the proposed model while keeping patients' privacy intact during collaborative model training by not sharing any patient's data. The proposed model is trained and tested on the aforementioned datasets and is deployed in a web-based clinical platform for disease diagnosis, prediction, and report generation.

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