Jayneet Jain
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jayneet Jain has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Mihika Kubadia
Department of Information Technology, Dwarkadas J Sanghvi College of Engineering, Mumbai 400056, Maharashtra, India
1 shared publication - Monika Mangla
Dwarkadas J. Sanghvi College of Engineering
1 shared publication - Prachi Tawde
Department of Information Technology, Dwarkadas J Sanghvi College of Engineering, Mumbai 400056, Maharashtra, India
1 shared publication
Latest Publications
- Open Access
- 4 January 2024
- Published by MDPI AG in Engineering Proceedings
- Vol. 59 (1) , 144
Comparison of Transfer Learning Techniques to Classify Brain Tumours Using MRI Images
Brain tumour detection and classification are life-saving steps for humanity. There are many medical imaging techniques that can identify abnormal brain diseases. These include nuclear magnetic resonance, ultrasound, X-rays, radionuclides, lasers, electrons, and light. Because of the outstanding image quality and lack of ionising radiation, magnetic resonance imaging (MRI) is widely employed in medical imaging. Artificial Intelligence provides an easier way to interpret these MRIs, which is otherwise a tedious and time-consuming task. Deep learning networks and convolutional neural networks have been very good in the detection of brain tumours. In this work, the authors employ deep-learning transfer techniques for the classification of brain tumours. The VGG-16, ResNet-50, and Inception v3 models with CNN pre-training have been utilised by the authors to predict and categorise brain tumours automatically. Using a dataset of 7023 MRI brain tumour images divided into four different classifications, pre-trained models are shown to be effective. The performance of the VGG-16, ResNet-50, and Inception v3 models is compared, and it is established from the experimental evaluation that ResNet-50 outperforms VGG-16 and Inception v3. Thus, the employment of ResNet-50 in tumour classification is validated and advocated.
- 4 January 2024
- Published by MDPI AG in Engineering Proceedings
- Vol. 59 (1) , 144
Open AccessComparison of Transfer Learning Techniques to Classify Brain Tumours Using MRI Images
Brain tumour detection and classification are life-saving steps for humanity. There are many medical imaging techniques that can identify abnormal brain diseases. These include nuclear magnetic resonance, ultrasound, X-rays, radionuclides, lasers, electrons, and light. Because of the outstanding image quality and lack of ionising radiation, magnetic resonance imaging (MRI) is widely employed in medical imaging. Artificial Intelligence provides an easier way to interpret these MRIs, which is otherwise a tedious and time-consuming task. Deep learning networks and convolutional neural networks have been very good in the detection of brain tumours. In this work, the authors employ deep-learning transfer techniques for the classification of brain tumours. The VGG-16, ResNet-50, and Inception v3 models with CNN pre-training have been utilised by the authors to predict and categorise brain tumours automatically. Using a dataset of 7023 MRI brain tumour images divided into four different classifications, pre-trained models are shown to be effective. The performance of the VGG-16, ResNet-50, and Inception v3 models is compared, and it is established from the experimental evaluation that ResNet-50 outperforms VGG-16 and Inception v3. Thus, the employment of ResNet-50 in tumour classification is validated and advocated.