An Efficient Deep Learning Framework for Accurate Disease Classification
Aruna Kokkula
Department of Electronics and Communication Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana, India and Department of Electronics and Communication Engineering, Maturi Venkata Subba Rao (MVSR) Engineering College, Nadargul, Hyderabad, Telangana, India.
One of the leading causes of memory loss and thinking problems in older adults is a condition that affects human function over time. Detecting this condition early is important for better care and treatment. However, even with the latest technology in artificial intelligence (AI) and deep learning, the results are not convincing because the dynamic nature of the datasets. This study introduces a new deep learning approach that includes a tool called Grad-CAM, which helps explain how the AI makes decisions. Our goal is to build a reliable and understandable system that uses a special type of AI model called a convolutional neural network (CNN) to analyze online dataset images. The model includes techniques to reduce errors and handle different types of data, while Grad-CAM provides visual feedback showing what the model is focusing on. The system achieved 95% accuracy, performing better than other well-known models like Xception (94.40%) and InceptionV3 (93.20%). Overall, this work offers a highly accurate and transparent tool to support early detection of memory-related conditions, assist professionals in planning care, and open new possibilities for research in AI-supported health applications.
Keywords
Deep Learning, Grad-CAM, Convolutional Neural Networks, Classification, Explainable AI.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Aruna Kokkula and Chandra Sekhar P;
Methodology: Chandra Sekhar P;
Software: Aruna Kokkula and Chandra Sekhar P;
Data Curation: Aruna Kokkula;
Writing- Original Draft Preparation: Aruna Kokkula;
Visualization: Aruna Kokkula and Chandra Sekhar P;
Investigation: Aruna Kokkula;
Supervision: Chandra Sekhar P;
Validation: Aruna Kokkula;
Writing- Reviewing and Editing: Aruna Kokkula and Chandra Sekhar P; All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to Dr. Chandra Sekhar P for this research completion and support.
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Aruna Kokkula
Department of Electronics and Communication Engineering, University College of Engineering, Osmania University, Hyderabad, Telangana, India and Department of Electronics and Communication Engineering, Maturi Venkata Subba Rao (MVSR) Engineering College, Nadargul, Hyderabad, Telangana, India.
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Cite this article
Aruna Kokkula and Chandra Sekhar P, “An Efficient Deep Learning Framework for Accurate Disease Classification”, Journal of Machine and Computing, vol.5, no.3, pp. 1532-1542, July 2025, doi: 10.53759/7669/jmc202505121.
