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International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
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ISSN: 3108-1754 (Online)
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ISO Certification: 9001:2015
Publication Fee: 599/- INR
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License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 6

Published on: June 2026

MULTI DISEASES PREDICTION USING DEEP LEARNING TECHNIQUES

Vivekadevi.R

Dr.N.Dhivya

Department of MCA, Vivekanandha Institute of Information and Management Studies Tiruchengode, Namakkal Tamilnadu, India.
DOI:https://doi.org/10.55041/ijcope.v2i6.195

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

In the modern healthcare landscape, early and accurate diagnosis is critical for effective treatment planning and patient outcomes. "Multi Disease Prediction Using Deep Learning Techniques" (MediPred AI) introduces an advanced diagnostic system designed to automate the detection of multiple medical conditions using state-of- the-art Deep Learning architectures. The project focuses on two primary domains: Brain Tumor classification (Glioma, Meningioma, Pituitary, and Normal) and Chest X-Ray analysis (Pneumonia and Normal). Leveraging Convolutional Neural Networks (CNNs), the system utilizes the DenseNet121 architecture through transfer learning to extract complex spatial features from medical imaging data. To enhance diagnostic precision, the models are fine-tuned via a multi-phase training process, unfreezing select layers to specialize in tumor-specific patterns and chest opacities. Furthermore, the system incorporates a MobileNetV2-based modality classifier to automatically identify the type of scan uploaded, ensuring images are routed to their respective specialized models and minimizing the risk of misdiagnosis. Equipped with data augmentation techniques and dynamic training callbacks, MediPred AI achieves high performance and robustness against data variability. The resulting application provides a seamless, automated workflow for medical image analysis, offering high- confidence diagnostic support to healthcare professionals and facilitating faster, data-driven clinical decisions.

Keywords: Deep Learning, Multi-Disease Prediction, Convolutional Neural Networks (CNN), DenseNet121, Brain Tumor Classification, Chest X-Ray Analysis, Pneumonia Detection

How to Cite this Paper

Vivekadevi.R, (2026). Multi Diseases Prediction Using Deep Learning Techniques. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(6). https://doi.org/10.55041/ijcope.v2i6.195

Vivekadevi.R, . "Multi Diseases Prediction Using Deep Learning Techniques." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i6.195.

Vivekadevi.R, . "Multi Diseases Prediction Using Deep Learning Techniques." International Journal of Creative and Open Research in Engineering and Management 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i6.195.

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References


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  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
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  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Jun 15 2026
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