Brain Tumour Detection Using Convolutional Neural Networks in Machine Learning: A Streamlit-Based Framework for MRI Image Analysis
K. Vishnu Vardhan1, R. Praveen Kumar2
1K. Vishnu Vardhan, Scholar, Computer Science and Engineering, Chaitanya Deemed to be University, Hyderabad, (Telangana), India.
2Dr. R. Praveen Kumar, Associate Professor, Computer Science and Engineering, Chaitanya Deemed to be University, Hyderabad, (Telangana), India.
Manuscript received on 19 September 2025 | First Revised Manuscript received on 02 March 2026 | Second Revised Manuscript received on 16 April 2026 | Manuscript Accepted on 15 May 2026 | Manuscript published on 30 May 2026 | PP: 1-6 | Volume-6 Issue-4 May 2026 | Retrieval Number: 100.1/ijpmh.A112906011125 | DOI: 10.54105/ijpmh.A1129.06040526
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Objectives: The deep learning model’s capacity to recognise brain cancers in MRI images. The model is intended to automatically analyse MRI scans and determine whether a tumour is present, producing reliable, accurate classification results. Methods: A previously trained CNN was used to classify MRI images. The MRI image in the dataset was first reduced and normalised during preprocessing to ensure accurate input to the model. After processing, the model produced a probability value indicating the likelihood that the image contained a tumour. Findings: The available MRI cases were correctly classified as tumours, and no non-tumour cases were identified. The prediction’s tumour probability of 99.74% indicates how confident the model was in its classification result. Novelty: This work demonstrates a CNN-based approach to identifying brain tumours from MRI data. Even with a small input sample, the system generated accurate and reliable predictions. The proposed method demonstrates how deep learning models could aid in cancer detection and potentially serve as a useful adjunct to clinical decision-making.
Keywords: Convolutional Neural Networks (CNNs), Deep Learning, Tumour Prediction, Computer-Aided Diagnosis, MRI Classification, Brain Tumour Detection, Artificial Intelligence in Healthcare, Image Processing, and Predictive Modelling.
Scope of the Article: Public Health