TY  - JOUR
AU  - Suthar, Om Prakash
AU  - Zinzuvadia, Yash
AU  - Ullah, Waqas
AU  - Khan, Haseeb
AU  - Agarwal, Chirag
PY  - 2025
DA  - 2025/03/20
TI  - Visual Intelligence in Neuro-Oncology: Effective Brain Tumor Detection through Optimized Convolutional Neural Networks
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 2
IS  - 1
SP  - 25
EP  - 35
DO  - 10.62762/TSCC.2024.964451
UR  - https://www.icck.org/article/abs/TSCC.2024.964451
KW  - deep learning
KW  - convolutional neural network
KW  - brain tumor classification
KW  - multilayer perceptron
KW  - detection system
KW  - AI
AB  - Brain tumor detection (BTD) is a crucial task, as early detection can save lives. Medical professionals require visual intelligence assistance to efficiently and accurately identify brain tumors. Conventional methods often result in misrecognition, highlighting a critical research gap. To address this, a novel BTD system is proposed to predict the presence of a tumor in a given MRI image. The system leverages a convolutional neural network (CNN) architecture, combined with a multi-layer perceptron (MLP) for feature extraction and understanding complex pixel patterns. An extensive ablation study was conducted to empirically analyze and identify the optimal model for the task. The findings demonstrate that the proposed method outperforms existing approaches. Notably, the model with two convolutional layers achieved an accuracy of 86.17%, while a single-layer model attained an impressive accuracy of 99.6%.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Suthar2025Visual,
  author = {Om Prakash Suthar and Yash Zinzuvadia and Waqas Ullah and Haseeb Khan and Chirag Agarwal},
  title = {Visual Intelligence in Neuro-Oncology: Effective Brain Tumor Detection through Optimized Convolutional Neural Networks},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2025},
  volume = {2},
  number = {1},
  pages = {25-35},
  doi = {10.62762/TSCC.2024.964451},
  url = {https://www.icck.org/article/abs/TSCC.2024.964451},
  abstract = {Brain tumor detection (BTD) is a crucial task, as early detection can save lives. Medical professionals require visual intelligence assistance to efficiently and accurately identify brain tumors. Conventional methods often result in misrecognition, highlighting a critical research gap. To address this, a novel BTD system is proposed to predict the presence of a tumor in a given MRI image. The system leverages a convolutional neural network (CNN) architecture, combined with a multi-layer perceptron (MLP) for feature extraction and understanding complex pixel patterns. An extensive ablation study was conducted to empirically analyze and identify the optimal model for the task. The findings demonstrate that the proposed method outperforms existing approaches. Notably, the model with two convolutional layers achieved an accuracy of 86.17\%, while a single-layer model attained an impressive accuracy of 99.6\%.},
  keywords = {deep learning, convolutional neural network, brain tumor classification, multilayer perceptron, detection system, AI},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}