MULTI DISEASE DETECTION USING DEEP LEARNING MODEL

Registration ID: IJNRD_305001

Published ID: IJNRD2503431

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Kantipudi Sneha, Kasani Hasiny, Kachavarapu Indhu, Nasira Mahjabeen

Pre-processing, cnn, dense, feature extraction, brain, lung, breast, stomach, and skin

This study is dedicated to detecting multiple diseases in human organs such as the brain, lungs, breasts, stomach, and skin by leveraging deep learning techniques. We implemented Convolutional Neural Networks (CNNs) to train a dataset, facilitating a comprehensive comparison and analysis, given that CNNs are highly effective for image recognition tasks. In our investigation, we utilized a CNN in conjunction with advanced image processing methods to scrutinize medical images. We evaluated the performance of our custom CNN model against dense models and found that, despite the limited size of our dataset, our model achieved remarkable accuracy, reaching 90% with minimal complexity. Unlike existing pre-trained models, our CNN model not only requires fewer computational resources but also achieves significantly higher accuracy.

Published Paper   E-Certificate

Kantipudi Sneha, Kasani Hasiny, Kachavarapu Indhu, & Nasira Mahjabeen (March-2025). MULTI DISEASE DETECTION USING DEEP LEARNING MODEL. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(3), e233-e236. https://ijnrd.org/papers/IJNRD2503431.pdf

Volume 10 Issue 3, March-2025

Pages : e233-e236

Paper Reg. ID: IJNRD_305001

Published Paper Id: IJNRD2503431

Downloads: 000121986

Research Area: Science and Technology

Author Type: Indian Author

Country: Hyderabad, Telangana, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2503431.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2503431

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Call For Paper - Volume 10 | Issue 11 | November 2025

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