Md. Habibur Rahman

Work place: Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail-1902, Bangladesh



Research Interests: Natural Language Processing


Md. Habibur Rahman received his B.Sc. (Engineering) degree in Information and Communication Technology (ICT) from Mawlana Bhashani Science and Technology University (MBSTU). His research interest includes Deep Neural Network (DNN), Natural Language Processing, Bioinformatics, etc.

Author Articles
Enhancing Brain Tumor Classification in MRI: Leveraging Deep Convolutional Neural Networks for Improved Accuracy

By Shourove Sutradhar Dip Md. Habibur Rahman Nazrul Islam Md. Easin Arafat Pulak Kanti Bhowmick Mohammad Abu Yousuf

DOI:, Pub. Date: 8 Jun. 2024

Brain tumors are among the deadliest forms of cancer, and there is a significant death rate in patients. Identifying and classifying brain tumors are critical steps in understanding their functioning. The best way to treat a brain tumor depends on its type, size, and location. In the modern era, Radiologists utilize Brain tumor locations that can be determined using magnetic resonance imaging (MRI). However, manual tests and MRI examinations are time-consuming and require skills. In addition, misdiagnosis of tumors can lead to inappropriate medical therapy, which could reduce their chances of living. As technology advances in Deep Learning (DL), Computer Assisted Diagnosis (CAD) as well as Machine Learning (ML) technique has been developed to aid in the detection of brain tumors, radiologists can now more accurately identify brain tumors. This paper proposes an MRI image classification using a VGG16 model to make a deep convolutional neural network (DCNN) architecture. The proposed model was evaluated with two sets of brain MRI data from Kaggle. Considering both datasets during the training at Google Colab, the proposed method achieved significant performance with a maximum overall accuracy of 96.67% and 97.67%, respectively. The proposed model was reported to have worked well during the training period and been highly accurate. The proposed model's performance criteria go beyond existing techniques.

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