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Intelligent Neurodiagnostic Platform for Brain Tumor and Alzheimer's Detection Using Deep Learning Models

Mr.Warghude Rushikesh Sudhakar, Mr.Gulve Rushikesh Somnath, Mr.Hase Onkar Balasaheb, Mr.Jadhav Pranav Prashant, Ms.K. T. Bhandwalkar

Abstract


The timely and precise identification of neurological conditions such as brain tumors and Alzheimer’s disease carries profound implications for patient survival, treatment efficacy, and long-term quality of life. This paper introduces NeuroDetect AI, a web-deployable Intelligent Neurodiagnostic Platform that automates MRI-based brain scan classification across brain-tumor-positive, Alzheimer’s-positive, and neurologically normal categories. The system adopts a dual deep learning strategy: a custom Modified Convolutional Neural Network for brain tumor classification and an EfficientNetB0 transfer-learning model for Alzheimer’s detection. A standardized preprocessing pipeline consisting of grayscale conversion, CLAHE, intensity normalization, and augmentation feeds both models. The platform uses a Flask REST API for inference and real-time doctor-patient communication, while a Django-backed module manages authentication, patient records, appointment scheduling, and role-based access control. Evaluation on 6,500 combined MRI scans from the Kaggle Brain Tumor MRI Dataset and ADNI yielded 96.9% accuracy for tumor detection and 95.8% for Alzheimer’s staging, with an average inference latency of 1.3 seconds per scan. The platform integrates confidence-based clinical triage, physician override, PDF report generation, and real-time consultation within a single deployable web application.


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References


S. I. Khan et al., “Accurate brain tumor detection using deep convolutional neural network,” Comput. Struct. Biotechnol. J., vol. 20, pp. 4733-4745, 2022.

S. Saeedi et al., “MRI-based brain tumor detection using convolutional deep learning methods,” Sci. Rep., vol. 13, 2023.

V. Mani et al., “Early-stage Alzheimer’s disease prediction using machine learning models,” Healthcare, vol. 10, no. 3, 2022.

A. G. Balamurugan et al., “Robust brain tumor classification by fusion of deep learning and channel-wise attention,” IEEE Access, vol. 12, 2024.

X. Liu and Z. Wang, “Deep learning in medical image classification from MRI-based brain tumor images,” IEEE Trans. Med. Imaging, vol. 43, 2024.

P. Sharma and S. Kulkarni, “Deep learning-based brain tumor classification using MRI images,” IEEE Trans. Biomed. Eng., vol. 70, no. 5, pp. 1245-1254, May 2023.

A. Patel and R. Nair, “Automated Alzheimer’s detection using deep learning and MRI scans,” IEEE Access, vol. 10, pp. 110245-110256, Dec. 2022.

S. Banerjee and T. Deshmukh, “Hybrid deep learning model for brain tumor segmentation and classification,” IEEE J. Transl. Eng. Health Med., vol. 11, pp. 89-98, 2023.

V. Iyer and N. Reddy, “Deep CNN-based Alzheimer’s disease stage prediction from MRI data,” IEEE Trans. Cogn. Dev. Syst., vol. 15, no. 3, pp. 445-455, 2023.

R. Gupta and T. Nambiar, “Cloud-based deep learning system for automated brain tumor and Alzheimer’s detection,” IEEE Access, vol. 12, pp. 110921-110933, Jan. 2024.


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