Intelligent Neurodiagnostic Platform for Brain Tumor and Alzheimer's Detection Using Deep Learning Models
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| Natura: | Recurso digital |
| Lingua: | inglese |
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2026
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| _version_ | 1866902168590614528 |
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| author | Mr.Gulve Rushikesh Somnath Mr.Hase Onkar Balasaheb Mr.Jadhav Pranav Prashant Mr.Warghude Rushikesh Sudhakar Ms.K. T. Bhandwalkar |
| author_facet | Mr.Gulve Rushikesh Somnath Mr.Hase Onkar Balasaheb Mr.Jadhav Pranav Prashant Mr.Warghude Rushikesh Sudhakar Ms.K. T. Bhandwalkar |
| contents | <p class="MsoNormal"><em><span>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,<span> </span>Alzheimer’s-positive,<span> </span>and<span> </span>neurologically<span> </span>normal<span> </span>categories.<span> </span>The<span> </span>system<span> </span>adopts<span> </span>a dual deep learning strategy:<span> </span>a custom Modified Convolutional Neural Network<span> </span>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<span> </span>and<span> </span>real-time<span> </span>doctor-patient<span> </span>communication,<span> </span>while<span> </span>a<span> </span>Django-backed<span> </span>module<span> </span>manages authentication, patient records, appointment scheduling, and role-based access control. Evaluation<span> </span>on<span> </span>6,500<span> </span>combined<span> </span>MRI<span> </span>scans<span> </span>from<span> </span>the<span> </span>Kaggle<span> </span>Brain<span> </span>Tumor<span> </span>MRI<span> </span>Dataset<span> </span>and<span> </span>ADNI yielded<span> </span>96.9%<span> </span>accuracy<span> </span>for<span> </span>tumor<span> </span>detection<span> </span>and<span> </span>95.8%<span> </span>for<span> </span>Alzheimer’s<span> </span>staging,<span> </span>with<span> </span>an<span> </span>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.</span></em></p> <p class="MsoBodyText"><em><span> </span></em></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20201511 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Intelligent Neurodiagnostic Platform for Brain Tumor and Alzheimer's Detection Using Deep Learning Models Mr.Gulve Rushikesh Somnath Mr.Hase Onkar Balasaheb Mr.Jadhav Pranav Prashant Mr.Warghude Rushikesh Sudhakar Ms.K. T. Bhandwalkar brain tumor detection, Alzheimer's disease classification, Modified CNN, EfficientNetB0, transfer learning, MRI analysis, Flask, Django, deep learning, clinical decision support, neurodiagnostics <p class="MsoNormal"><em><span>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,<span> </span>Alzheimer’s-positive,<span> </span>and<span> </span>neurologically<span> </span>normal<span> </span>categories.<span> </span>The<span> </span>system<span> </span>adopts<span> </span>a dual deep learning strategy:<span> </span>a custom Modified Convolutional Neural Network<span> </span>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<span> </span>and<span> </span>real-time<span> </span>doctor-patient<span> </span>communication,<span> </span>while<span> </span>a<span> </span>Django-backed<span> </span>module<span> </span>manages authentication, patient records, appointment scheduling, and role-based access control. Evaluation<span> </span>on<span> </span>6,500<span> </span>combined<span> </span>MRI<span> </span>scans<span> </span>from<span> </span>the<span> </span>Kaggle<span> </span>Brain<span> </span>Tumor<span> </span>MRI<span> </span>Dataset<span> </span>and<span> </span>ADNI yielded<span> </span>96.9%<span> </span>accuracy<span> </span>for<span> </span>tumor<span> </span>detection<span> </span>and<span> </span>95.8%<span> </span>for<span> </span>Alzheimer’s<span> </span>staging,<span> </span>with<span> </span>an<span> </span>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.</span></em></p> <p class="MsoBodyText"><em><span> </span></em></p> |
| title | Intelligent Neurodiagnostic Platform for Brain Tumor and Alzheimer's Detection Using Deep Learning Models |
| topic | brain tumor detection, Alzheimer's disease classification, Modified CNN, EfficientNetB0, transfer learning, MRI analysis, Flask, Django, deep learning, clinical decision support, neurodiagnostics |
| url | https://doi.org/10.5281/zenodo.20201511 |