Brain Tumor Classification Using Vanilla Convolutional Neural Networks

Authors

  • Md Najmul Huda Department of Computer Science, KICT, International Islamic University Malaysia, Kuala Lumpur, Malaysia
  • Akeem Olowolayemo Department of Computer Science, International Islamic University Malaysia, Kuala Lumpur, Malaysia
  • Ayesha Dupe Adeleye Kulliyyah of Information & Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia
  • Amin Nur Rashid Department of Computer Science, International Islamic University Malaysia, Kuala Lumpur, Malaysia
  • Abrar Habib Haque Department of Computer Science, International Islamic University Malaysia, Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.31436/ijpcc.v10i2.455

Keywords:

Brain tumour, CNN, Classification, Deep learning, MRI, Vanilla-CNN.

Abstract

Brain tumours are a common and dangerous type of malignant tumour that, if not detected early enough, can cut short a patient's life. The segmentation and classification of brain tumours using solely traditional medical image processing is a difficult and time-consuming task. Various imaging modalities, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound image, are frequently utilized to assess brain, lung, liver, breast, prostate and other tumours. MRI images are specifically utilised in these analyses to detect brain tumours. As a result, developing approaches for detecting, recognizing, and classifying the conditions based on image analysis becomes essential. A comprehensive and automatic classification system is important to saving human lives. The geographical and anatomical heterogeneity of brain tumours makes automatic categorization challenging. This study proposes an automated method for detecting brain tumours using Convolutional Neural Networks (CNNs) classification, with the primary goal of developing a deep learning model that is capable of accurately identifying and classifying images as either having a brain tumour or not. In this paper, we provide a classification model for brain tumours based on a Deep Convolutional Neural Network with a vanilla neural network technique. The proposed method's performance on a publicly available dataset of 3000 Brain MRI Images yielded superior results, with accuracy and F1 score of 98.00 percent and 98.00 percent, respectively. This study shows that the proposed vanilla-CNN model can be used to make it easier for brain tumours to be automatically classified

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Published

30-07-2024

How to Cite

Huda, M. N. ., Olowolayemo, A., Adeleye, A. D. ., Nur Rashid, A., & Haque, A. H. . (2024). Brain Tumor Classification Using Vanilla Convolutional Neural Networks. International Journal on Perceptive and Cognitive Computing, 10(2), 80–86. https://doi.org/10.31436/ijpcc.v10i2.455

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