Accurate Deep Learning Models for Predicting Brain Cancer at begin Stage

Sathish N; Gangadevi G; Sangeetha K; Niharikha Srinivasan

doi:10.54392/irjmt2536

Authors

Sathish N Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, India. Author https://orcid.org/0000-0003-4153-8176
Gangadevi G Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, India Author
Sangeetha K Department of Computer Science and Engineering, Panimalar Engineering College, India Author
Niharikha Srinivasan Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, India Author

DOI:

https://doi.org/10.54392/irjmt2536

Keywords:

Deep learning, Brain tumors, MRI images, Classification, Medical diagnostics

Abstract

The objective of this research is to explore and compare the performance of several Deep Learning (DL) models and identify the most accurate classification model for predicting brain tumors using MRI images. The research utilizes the dataset of 450 MRI images which include healthy cases, cases with grade 1 & 2 benign tumors, and cases with grade 3 & 4 malignant tumor cases. The dataset is further divided into the training, validation, and testing sets. Each model is then trained and validated on the training and validation sets and further tested on the testing set and the overall performance is assessed and compared. The results demonstrated unique trends among the models, where CNN and ResNet50 have consistently performed the best with the highest accuracy and least data loss. VGG16 and VGG19 have also exemplified great results, although they utilised more epochs to achieve similar accuracy. Based on the results of the study, it is concluded that the appropriate DL architecture for tumor classification should be selected especially in medical fields. In general, CNN and residual networks showed the best performance and should be chosen when accurate tumor classification is the most important requirement. The potential application of the outcomes of the research can be applied in the field of medicine mainly for the identification, classification, detection, and prediction of various diseases.

References

S. Anantharajan, S. Gunasekaran, T. Subramanian, R. Venkatesh, MRI brain tumor detection using deep learning and machine learning approaches. Measurement: Sensors, 31, (2024) 101026. https://doi.org/10.1016/j.measen.2024.101026

L. Gaur, M. Bhandari, T. Razdan, S. Mallik, Z. Zhao, Explanation-driven deep learning model for prediction of brain tumour status using MRI image data. Frontiers in genetics, 13, (2022) 822666. https://doi.org/10.3389/fgene.2022.822666

S.K. Mathivanan, S. Sonaimuthu, S. Murugesan, H. Rajadurai, B.D. Shivahare, M.A. Shah, Employing deep learning and transfer learning for accurate brain tumor detection. Scientific Reports, 14(1), (2024) 7232. https://doi.org/10.1038/s41598-024-57970-7

K.R.M. Fernando, C.P. Tsokos, Deep and statistical learning in biomedical imaging: State of the art in 3D MRI brain tumor segmentation. Information Fusion, 92, (2022) 450–465. https://doi.org/10.1016/j.inffus.2022.12.013

H. ZainEldin, S.A. Gamel, E.S.M. El-Kenawy, A.H. Alharbi, D.S. Khafaga, A. Ibrahim, F.M. Talaat, Brain tumor detection and classification using deep learning and sine-cosine fitness grey wolf optimization. Bioengineering, 10(1), (2022) 18. https://doi.org/10.3390/bioengineering10010018

F.J. Dorfner, J.B. Patel, J. Kalpathy-Cramer, E.R. Gerstner, C.P. Bridge, A review of deep learning for brain tumor analysis in MRI. NPJ Precision Oncology, 9(1), (2025) 2. https://doi.org/10.1038/s41698-024-00789-2

S. Patil, D. Kirange, Ensemble of deep learning models for brain tumor detection. Procedia Computer Science, 218, (2023) 2468-2479. https://doi.org/10.1016/j.procs.2023.01.222

A. Alshuhail, A. Thakur, R. Chandramma, T.R. Mahesh, A. Almusharraf, V. Vinoth Kumar, S.B. Khan, Refining neural network algorithms for accurate brain tumor classification in MRI imagery. BMC Medical Imaging, 24(1), (2024) 118. https://doi.org/10.1186/s12880-024-01285-6

F. Venesius, Y. Sie, R. Fredyan, H. Pranoto, (2023) Deep learning-based brain tumor prediction: An analysis of performance evaluation of convolutional neural network. In 2023 15th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter), IEEE, Indonesia. https://doi.org/10.1109/IIAI-AAI-Winter61682.2023.00046

S. Srinivasan, D. Francis, S.K. Mathivanan, H. Rajadurai, B.D. Shivahare, M.A. Shah, A hybrid deep CNN model for brain tumor image multi-classification. BMC Medical Imaging, 24(1), (2024) 21. https://doi.org/10.1186/s12880-024-01195-7

M.M. Emam, N.A. Samee, M.M. Jamjoom, E.H. Houssein, Optimized deep learning architecture for brain tumor classification using improved Hunger Games Search Algorithm. Computers in Biology and Medicine, 160, (2023) 106966. https://doi.org/10.1016/j.compbiomed.2023.106966

P. Kanchanamala, K.G. Revathi, M.B.J. Ananth, Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI. Biomedical Signal Processing and Control, 84, (2023) 104955. https://doi.org/10.1016/j.bspc.2023.104955

G. Satyanarayana, P. Appala Naidu, V. Subbaiah Desanamukula, K. Satish kumar, B. Chinna Rao, A mass correlation based deep learning approach using deep convolutional neural network to classify the brain tumor. Biomedical signal processing and control, 81, (2023) 104395. https://doi.org/10.1016/j.bspc.2022.104395

S. Sarkar, S. Bera, P. Moitra, S. Bhattacharya, A self-healable and injectable hydrogel for pH-responsive doxorubicin drug delivery in vitro and in vivo for colon cancer treatment. Materials Today Chemistry, 30, (2023) 101554. https://doi.org/10.1016/j.mtchem.2023.101554

V. Bhardwaj, S. Pandey, E. Bhardwaj, N. Sharma, (2024) Innovative Deep-Learning Method for MRI-Based Autonomous Alzheimer’s Disorder Identification. 2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), Chennai, India. https://doi.org/10.1109/ACCAI61061.2024.10602431

A.B. Abdusalomov, M. Mukhiddinov, T.K. Whangbo, Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging. Cancers, 15(16), (2023) 4172. https://doi.org/10.3390/cancers15164172

M.M. Rahman, F. Khatun, S I. Sami, A. Uzzaman, The evolving roles and impacts of 5G enabled technologies in healthcare: The world epidemic COVID-19 issues. Array, 14, (2022) 100178. https://doi.org/10.1016/j.array.2022.100178

K.S. Ananda Kumar, A.Y. Prasad, J. Metan, A hybrid deep CNN-Cov-19-Res-Net Transfer learning architype for an enhanced Brain tumor Detection and Classification scheme in medical image processing. Biomedical Signal Processing and Control, 76, (2022) 103631. https://doi.org/10.1016/j.bspc.2022.103631

The dataset used in this study was sourced from Kaggle’s Brain MRI Images dataset (2023). https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset

O. Turk, D. Ozhan, E. Acar, T. C. Akinci, M.Yilmaz, Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images. Zeitschrift für Medizinische Physik, 34(2), (2024) 278-290. https://doi.org/10.1016/j.zemedi.2022.11.010

A. Nithya, M. Raja, D. Latha, M. Preetha, M. Karthikeyan, G.S. Uthayakumar, (2023) Artificial Intelligence on Mobile Multimedia Networks for Call Admission Control Systems. 4th International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India. https://doi.org/10.1109/ICOSEC58147.2023.10275999

S.M. Patil, L. Tong, M.D. Wang, (2020) Generating Region of Interests for Invasive Breast Cancer in Histopathological Whole-Slide-Image. Proceedings 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC, IEEE, Spain. https://doi.org/10.1109/COMPSAC48688.2020.0-174

K.R. Pedada, A. Bhujanga Rao, K.K. Patro, J.P. Allam, M.M. Jamjoom, N.A. Samee, A novel approach for brain tumour detection using deep learning based technique. Biomedical Signal Processing and Control, 82, (2021) 104549. https://doi.org/10.1016/j.bspc.2022.104549

K. Zlobina, M. Jafari, M. Rolandi, M. Gomez, The role of machine learning in advancing precision medicine with feedback control. Cell Reports Physical Science, 3(11), (2022) 101149. https://doi.org/10.1016/j.xcrp.2022.101149

K Sivakumar, M. Sughasiny, K.K.Thyagarajan, A. Karthikeyan, K. Sangeetha, A Comparative Analysis of GOA (Grasshopper Optimization Algorithm) Adversarial Deep Belief Neural Network for Renal Cell Carcinoma: Kidney Cancer Detection & Classification. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), (2024) 43–48.

A.E. Baker, L.C. Bahlmann, C. Xue, Y.H.J. Lu, A.A. Chin, J. Cruickshank, M.S. Shoichet, Chemically and mechanically defined hyaluronan hydrogels emulate the extracellular matrix for unbiased in vivo and in vitro organoid formation and drug testing in cancer. Materials Today, 56, (2022) 96-113. https://doi.org/10.1016/j.mattod.2022.01.023

M.A. Talukder, M.M. Islam, M.A. Uddin, A. Akhter, M.A.J. Pramanik, S. Aryal, M.A.A. Almoyad, K.F. Hasan, M.A. Moni, An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning. Expert systems with applications, 230, (2023) 120534. https://doi.org/10.1016/j.eswa.2023.120534

B. Subha, I.A.K. Shaikh, P.J. Patil, R. Sethumadhavan, M. Preetha, H. Patil, (2023) Predictive Analysis of Employee Turnover in IT Using a Hybrid CRF-BiLSTM and CNN Model. In 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA), IEEE, India. https://doi.org/10.1109/ICSCNA58489.2023.10370093

P. Razzaghi, K. Abbasi, M. Shirazi, S.Rashidi, Multimodal brain tumor detection using multimodal deep transfer learning. Applied Soft Computing, 129, (2022) 109631. https://doi.org/10.1016/j.asoc.2022.109631

K.S. Kumar, T. Mayavan, S. Sambath, A. Kadirvel, T.F. Gladson, S. Senthilkumar, Artificial Neural Networks to the Analysis of AISI 304 steel sheets through limiting Drawing Ratio test. Journal of Electrical Systems, 3463, (2024) 2282-2291. https://doi.org/10.52783/jes.3463