Improving Brain Tumor MRI Image Classification Prediction based on Fine-tuned MobileNet

Brain tumors are a prevalent issue in contemporary society as they impact human health. The location of the tumor in the brain determines the variety of symptoms that may manifest. Some frequent symptoms are cephalalgia, convulsions, visual impairments, nausea, emesis, asthenia, paresthesia, dysphasia, personality alterations, and amnesia. The prognosis for brain cancer differs considerably depending on the cancer type. Nevertheless, brain tumors are amenable to treatment with surgical intervention, chemotherapy, and radiotherapy if the diagnosis is timely. Furthermore, artificial intelligence and machine learning can assist in the detection of brain tumors as they have significant implications for the analysis of Magnetic Resonance Imaging (MRI). To accomplish this objective, automated measurement instruments were proposed based on the processing of MRI. In this study, we employed the latest developments in deep transfer learning and fine-tuning to identify tumors without many complex steps. We gathered data from authentic MRI of 3264 subjects (i.e., 926 glioma tumors, 937 meningioma tumors, 901 pituitary tumors, and 500 normal). With the MobileNet model from the Keras library, we attained the highest validation accuracy, test accuracy, and F1 score in four-class classifications was 97.24%, 97,86%, and 97.85%, respectively. Concerning two-class classification, high accuracy values were obtained for most of the models (i.e., ~100%). These outcomes and other performance indicators demonstrate a strong capability to diagnose brain tumors from conventional MRI. The current research developed a supportive machine learning that can aid doctors in making the accurate diagnosis with less time and mistakes.

@article{Lu2024,
  title     = {Improving Brain Tumor MRI Image Classification Prediction based on Fine-tuned MobileNet},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {1},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Quy Thanh Lu and Triet Minh Nguyen and Huan Le Lam},
  doi       = {10.14569/IJACSA.2024.0150152},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150152}
}