Design and Implementation of an AI-powered CT Scan Report Analyzer for Web and Mobile Platforms
Abstract
Brain tumors are one of the most life-threatening neurological disorders, requiring early and accurate diagnosis for effective treatment planning. Computed tomography (CT) remains one of the most reliable and widely accessible imaging modalities for brain screening, especially in emergency and resource-limited healthcare settings. However, manual interpretation of CT images is highly subjective and prone to variability among radiologists, leading to delays in diagnosis and potential misclassification of tumor regions. This paper presents a highly scalable, intelligent, and explainable AI-based CT scan analyzer (Web/App) that leverages hybrid deep learning and modern web technologies for automated tumor detection and visualization. The proposed system combines three major deep learning paradigms—Convolutional neural networks (CNN) for spatial feature extraction, genetic algorithm (GA) for feature selection and optimization, and bidirectional long short-term memory (BiLSTM) for temporal and contextual classification. The model is deployed via a ReactJS frontend and a Flask-TensorFlow backend, creating a responsive web application capable of real-time inference, visualization through Grad-CAM heatmaps, and secure data handling via MongoDB. This study consolidates literature between 2022 and 2025 and discusses the methodology, architecture, explainability, deployment, and clinical implications of integrating hybrid AI systems in radiological diagnostics.
References
B. Abdusalomov, M. Mukhiddinov, and T. K. Whangbo, “Brain tumor detection based on deep learning approaches and magnetic resonance imaging,” Cancers, vol. 15, no. 16, p. 4172, Aug. 2023.
V. K. Dhakshnamurthy, M. Govindan, K. Sreerangan, M. D. Nagarajan, and A. Thomas, “Brain tumor detection and classification using transfer learning models,” Engineering Proceedings, vol. 62, 2024.
M. J. Hasan, M. Hasan, S. Akter, A. B. S. Mahi, and M. P. Uddin, “Enhancing brain tumor classification with a novel attention-based explainable deep learning framework,” Biomedical Signal Processing and Control, vol. 112, p. 108636, Sep. 2025.
M. F. Dar and A. Ganivada, “Deep learning and genetic algorithm-based ensemble model for feature selection and classification of breast ultrasound images,” Image and Vision Computing, vol. 146, p. 105018, Jun. 2024.
E. Y. Hidayat, Y. P. Astuti, I. N. Dewi, A. Salam, M. A. Soeleman, Z. A. Hasibuan, and A. S. Yousif, “Genetic algorithm-based convolutional neural network feature engineering for optimizing coronary heart disease prediction performance,” Healthcare Informatics Research, vol. 30, no. 3, pp. 234–243, Jul. 2024.
M. S. Remya, P. Ishwar, and P. Nedungadi, “A hybrid cross-attentive CNN-BiLSTM-transformer network for dysarthria severity classification,” Scientific Reports, vol. 15, no. 1, Nov. 2025.
S. Wang, et al., “Autoregressive sequence modeling for 3D medical image representation,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), Philadelphia, PA, USA, Feb. 27–Mar. 5, 2025, vol. 39, no. 8, pp. 7871–7879.
K. Borys, et al., “Explainable AI in medical imaging: An overview for clinical practitioners – Saliency-based XAI approaches,” European Journal of Radiology, vol. 162, p. 110787, May 2023.
R. Miotto, F. Wang, S. Wang, X. Jiang, and J. T. Dudley, “Deep learning for healthcare: Review, opportunities and challenges,” Briefings in Bioinformatics, vol. 19, no. 6, pp. 1236–1246, Nov. 2018.
W. Valenzuela, F. Balsiger, R. Wiest, and O. Scheidegger, “Medical-Blocks—A platform for exploration, management, analysis, and sharing of data in biomedical research: System development and integration results,” JMIR Formative Research, vol. 6, no. 4, Apr. 2022.
P. Korfiatis, et al., “Implementing artificial intelligence algorithms in the radiology workflow: Challenges and considerations,” Mayo Clinic Proceedings Digital Health, vol. 2, no. 4, pp. 100188, Dec. 2024.
Singh, S. Sengupta, and V. Lakshminarayanan, “Explainable deep learning models in medical image analysis,” Journal of Imaging, vol. 6, no. 6, p. 52, Jun. 2020.
L. Kamala and K. G. Mohan, “An efficient hybrid artificial intelligence framework for lung cancer classification using CT images,” Scientific Reports, vol. 16, p. 1777, 2026.
Z. L. Teo, L. Jin, S. Li, D. Miao, X. Zhang, W. Y. Ng, T. F. Tan, D. M. Lee, K. J. Chua, J. Heng, Y. Liu, R. S. M. Goh, and D. S. W. Ting, “Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture,” Cell Reports Medicine, vol. 5, no. 2, p. 101419, Feb. 2024.
G. Yang, Z. Zeng, X. Pu, and R. Duan, “Feature selection optimization algorithm based on evolutionary Q-learning,” Information Sciences, vol. 719, p. 122441, Nov. 2025.
S. Natha, F. Ahmed, M. Siraj, M. Lagari, M. Altamimi, and A. A. Chandio, “Deep BiLSTM attention model for spatial and temporal anomaly detection in video surveillance,” Sensors, vol. 25, no. 1, p. 251, Jan. 2025.
Mello-Thoms and C. A. B. Mello, “Clinical applications of artificial intelligence in radiology,” British Journal of Radiology, vol. 96, no. 1150, Oct. 2023.
R. Ghasemi, N. Islam, S. Bayat, M. Shabir, S. Rahman, F. Amin, I. de la Torre, Á. K. Castilla, and D. L. R. V. García, “Detection and classification of brain tumor using a hybrid learning model in CT scan images,” Scientific Reports, vol. 15, no. 1, Oct. 2025.
S. Aksoy, P. Demircioglu, and I. Bogrekci, “A web-deployed, explainable AI system for comprehensive brain tumor diagnosis,” Neurology International, vol. 17, no. 8, Aug. 2025.
World Health Organization, Global Cancer Observatory, Lyon, France: International Agency for Research on Cancer, 2022.
M. A. Saleem, et al., “Innovations in stroke identification: A machine learning-based diagnostic model using neuroimages,” IEEE Access, vol. 12, pp. 35754–35764, 2024.
R. Y. Abdullah, C. Venkatesan, E. Naresh, et al., “AI-driven hybrid convolutional and transformer-based deep learning architecture for precise lung nodule classification,” Scientific Reports, vol. 16, p. 3257, 2026.
J. P. M. Priyadarsini, K. Kotecha, G. K. Rajini, K. Hariharan, U. R. K., B. R. K., V. Indragandhi, V. Subramaniyaswamy, and S. Pandya, “Lung diseases detection using various deep learning algorithms,” Journal of Healthcare Engineering, vol. 2023, Art. no. 3563696, Feb. 2023.
