A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Oncology

Background:

• Early brain tumor detection is critical for patient survival and treatment efficacy.
• Manual tumor detection from MRI scans is challenging, time-consuming, and prone to errors.
• There is a need for automated diagnostic tools for accurate and efficient brain tumor identification.

Purpose of the Study:

• To develop and evaluate a novel deep learning model, TumorResNet, for automated brain tumor detection.
• To classify brain MRI scans as either normal or tumorous using binary classification.
• To improve the accuracy and efficiency of brain tumor diagnosis.

Main Methods:

• A deep learning (DL) model named TumorResNet was developed, featuring 20 convolutional layers with leaky ReLU activation.
• The model utilizes three fully connected layers for binary classification of brain MRI scans.
• Performance was evaluated on the Kaggle brain tumor MRI dataset for brain tumor detection (BTD).

Main Results:

• The TumorResNet model achieved a high accuracy of 99.33% in detecting brain tumors.
• Experimental results, including cross-dataset evaluations, demonstrated the superiority of TumorResNet over existing frameworks.
• The model effectively identified distinctive deep features for accurate classification.

Conclusions:

• The proposed TumorResNet offers a highly accurate automated method for brain tumor detection from MRI scans.
• This automated approach can significantly aid in the early diagnosis of brain cancers.
• The study highlights the potential of DL models to improve treatment strategies and enhance patient survival rates.