International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Unified AI Based Diagnostic Framework for Automatic Detection of Haematological Diseases Including Dengue, Malaria and Blood Cancer

Author(s) Ms. Saraswathula Sai Harshitha, G. Umamaheswara Reddy
Country India
Abstract Hematological diseases such as Dengue, Malaria, and Blood Cancer present significant diagnostic challenges, particularly in resource-limited settings where timely and expert analysis is critical. To overcome these limitations, this project proposes an Artificial Intelligence (AI) driven Diagnostic system is designed to automatically identify multiple hematological diseases like Dengue, Malaria, and Blood Cancer using blood smear images and structured CSV data. For Dengue detection, the framework employs the Random Forest algorithm on feature-extracted CSV data derived from blood parameters, offering an efficient and interpretable alternative to image-based analysis. In this project pre-trained convolution neural networks are used for malaria and blood cancer i;e VGG19 and EfficientnetB3 and for dengue machine learning technique is used. A unified model integrates these approaches, enhancing diagnostic accuracy, interpretability, and scalability. A user-friendly web interface was developed to provide real-time predictions for all three diseases, in order to improve clinical accessibility. Experimental results validate the framework’s effectiveness, demonstrating high accuracy for Dengue via Random Forest and robust performance for Malaria and Blood Cancer through VGG19 and EfficientNetB3.This project ensures the scalability and clinical relevant diagnostics in real-world development.
Field Medical / Pharmacy
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-11
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.62650
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E-ISSN 2582-2160
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