Enhanced Automated Curative System for Auxiliary Brain Tumor Detection Using ML Techniques

J. Raghunath; S. Fairoz; M. Lavanya; T. Chennakesava Reddy; N. Divya; P.R. Gururaj; V.N. Krishna Vamsi; B. Bharath Kumar

doi:https://doi.org/10.47001/ICJES/2025.402003

Authors

J. Raghunath

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

S. Fairoz

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

M. Lavanya

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

T. Chennakesava Reddy

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

N. Divya

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

P.R. Gururaj

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

V.N. Krishna Vamsi

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

B. Bharath Kumar

Department of Computer Science Engineering (Artificial Intelligence), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Abstract

Brain tumors remain a critical health challenge, where early detection and effective treatment significantly improve survival rates. However, patients in rural or underdeveloped areas often face limited access to specialists, leading to delayed diagnoses and worsening conditions. Traditional methods like MRI scans and biopsies are time-consuming, expensive, and dependent on expert analysis.

This project presents an intelligent, automated system for brain tumor detection and treatment support using advanced Machine Learning (ML) techniques. At its core is a Convolutional Neural Network (CNN) model trained on a large, labeled dataset of MRI brain scans. Users can upload MRI images through a simple, user-friendly interface, with real-time processing powered by cloud infrastructure.

The system accurately detects tumors and suggests possible treatment options based on the tumor’s type, location, and stage. It evolves continuously through expert feedback and new data inputs. Medical professionals can access a dedicated web portal to track tumor trends, review outcomes, and contribute to case evaluations.

Keywords

Automated Curative System Brain Tumor Detection ML Techniques Convolutional Neural Network CNN MRI brain scans Machine Learning ML

Citation of this Article

J. Raghunath, S. Fairoz, M. Lavanya, T. Chennakesava Reddy, N. Divya, P.R. Gururaj, V.N. Krishna Vamsi, & B. Bharath Kumar. (2025). Enhanced Automated Curative System for Auxiliary Brain Tumor Detection Using ML Techniques. International Current Journal of Engineering and Science - ICJES, 4(2), 12-17. Article DOI: https://doi.org/10.47001/ICJES/2025.402003

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[1] M. A. Rundo, S. Han, M. Nagano, and H. Higaki, "Artificial Intelligence in Brain Tumor Imaging: A Step toward Precision Medicine," Cancers, vol. 15, no. 3, p. 10047107, 2023.

[2] S. Hossain, M. M. Rahman, and M. A. Rahman, “Advancing brain tumor detection: A thorough investigation of CNNs, clustering, and SoftMax classification in the analysis of MRI images,” arXiv preprint, arXiv:2310.17720, Oct. 2023.

[3] S. H. A. et al., "Texture feature analysis of MRI-ADC images to differentiate glioma grades," Scientific Reports, vol. 13, no. 1, p. 12345, 2023.

[4] C. Mu, Z. Guo, and Y. Liu, “A Multi-Scale and Multi-Level Spectral-Spatial Feature Fusion Network for Hyperspectral Image Classification,” Remote Sensing, vol. 12, no. 1, p. 125, Jan. 2020.

[5] W. Wang, F. Jia, Y. Yang, K. Mu, and H. Liu, “MDSCNN: Remote Sensing Image Spatial–Spectral Fusion Method via Multi-Scale Dual-Stream Convolutional Neural Network,” Remote Sensing, vol. 16, no. 19, p. 3583, 2024.

[6] M. R. Keyvanpour, M. B. Shirzad, and L. Mahdikhani, ‘‘WARM: A new breast masses classification method by weighting association rule mining,’’ Signal, Image Video Process., vol. 16, no. 2, pp. 481–488, Mar. 2022.

[7] S. K. Jha, J. Wang, and R. Shanmugam, ‘‘An accurate soft diagnosis method of breast cancer using the operative fusion of derived features and classification approaches,’’ Expert Syst., vol. 39, no. 7, Aug. 2022, Art. no. e12976.

[8] K. S. Rekha and S. A. M. J. Amali, ‘‘Efficient feature subset selection and classification using Levy flight-based cuckoo search optimization with parallel support vector machine for the breast cancer data,’’ Int. J. Imag. Syst. Technol., vol. 32, no. 3, pp. 869–881, May 2022.

[9] B. Sowan, M. Eshtay, K. Dahal, H. Qattous, and L. Zhang, ‘‘Hybrid PSO feature selection-based association classification approach for breast cancer detection,’’ Neural Comput. Appl., vol. 35, no. 7, pp. 5291–5317, Mar. 2023.

[10] S. Alsubai, A. Alqahtani, and M. Sha, ‘‘Genetic hyperparameter optimization with modified scalable-neighbourhood component analysis for breast cancer prognostication,’’ Neural Netw., vol. 162, pp. 240–257, May 2023.

[11] M. Abdar, M. Zomorodi-Moghadam, X. Zhou, R. Gururajan, X. Tao, P. D. Barua, and R. Gururajan, ‘‘A new nested ensemble technique for automated diagnosis of breast cancer,’’ Pattern Recognit. Lett., vol. 132, pp. 123–131, Apr. 2020.

[12] S. Vijayalakshmi, A. John, R. Sunder, S. Mohan, S. Bhattacharya, R. Kaluri, G. Feng, and U. Tariq, ‘‘Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting,’’ Int. J. Distrib. Sensor Netw., vol. 16, no. 11, Nov. 2020, Art. no. 155014772097150.

[13] A.Kumar, N. Sinha, and A. Bhardwaj, ‘‘A novel fitness function in genetic programming for medical data classification,’’ J. Biomed. Informat., vol. 112, Dec. 2020, Art. no. 103623.

[14] S. A. El-Rahman, ‘‘Predicting breast cancer survivability based on machine learning and features selection algorithms: A comparative study,’’ J. Ambient Intell. Hum. Comput., vol. 12, no. 8, pp. 8585–8623, Aug. 2021.

[15] P. Stephan, T. Stephan, R. Kannan, and A. Abraham, ‘‘A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis,’’ Neural Comput. Appl., vol. 33, no. 20, pp. 13667–13691, Oct. 2021.

[16] U. Naseem, J. Rashid, L. Ali, J. Kim, Q. E. U. Haq, M. J. Awan, and M. Imran, ‘‘An automatic detection of breast cancer diagnosis and prognosis based on machine learning using ensemble of classifiers,’’ IEEE Access, vol. 10, pp. 78242–78252, 2022.

[17] E. Badr, S. Almotairi, M. A. Salam, and H. Ahmed, ‘‘New sequential and parallel support vector machine with grey wolf optimizer for breast cancer diagnosis,’’ Alexandria Eng. J., vol. 61, no. 3, pp. 2520–2534, Mar.2022.