article · Bioengineering
Brain tumour diagnosis is often time-consuming and relies heavily on radiologist expertise, while rising patient numbers make conventional review methods costly and inefficient. To address this challenge, an automated deep learning framework called the Brain Tumor Classification Model based on CNN (BCM-CNN) was developed. The system combines an Inception-ResnetV2 architecture with an adaptive dynamic sine-cosine fitness grey wolf optimizer (ADSCFGWO). This optimisation approach blends elements of sine cosine and grey wolf algorithms to tune structural and training hyperparameters. The model provides binary classification, labelling an image as either normal or tumour. Evaluated on the BRaTS 2021 Task 1 dataset, the optimised network achieved an accuracy of 99.98 percent, demonstrating high diagnostic precision through algorithmic tuning.
Accurate and rapid interpretation of medical scans is critical as patient volumes rise. Manual evaluation of brain scans is labour-intensive, costly, and heavily reliant on specialist experience. Automated deep learning systems capable of screening scans with high accuracy can reduce diagnostic delays, ease the workload on healthcare specialists, and provide fast, dependable second opinions in clinical image analysis.
The model represents a software-based diagnostic aid designed for radiologists and medical imaging departments. Having been tested on the BRaTS 2021 Task 1 dataset, the technology is at the applied research and benchmarked testing stage. Moving towards real-world hospital deployment would require integration into clinical radiology workflows, testing across varied hospital imaging platforms, and formal regulatory evaluation for medical device software.
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Diagnosing a brain tumor takes a long time and relies heavily on the radiologist's abilities and experience. The amount of data that must be handled has increased dramatically as the number of patients has increased, making old procedures both costly and ineffective. Many researchers investigated a variety of algorithms for detecting and classifying brain tumors that were both accurate and fast. Deep Learning (DL) approaches have recently been popular in developing automated systems capable of accurately diagnosing or segmenting brain tumors in less time. DL enables a pre-trained Convolutional Neural Network (CNN) model for medical images, specifically for classifying brain cancers. The proposed Brain Tumor Classification Model based on CNN (BCM-CNN) is a CNN hyperparameters optimization using an adaptive dynamic sine-cosine fitness grey wolf optimizer (ADSCFGWO) algorithm. There is an optimization of hyperparameters followed by a training model built with Inception-ResnetV2. The model employs commonly used pre-trained models (Inception-ResnetV2) to improve brain tumor diagnosis, and its output is a binary 0 or 1 (0: Normal, 1: Tumor). There are primarily two types of hyperparameters: (i) hyperparameters that determine the underlying network structure; (ii) a hyperparameter that is responsible for training the network. The ADSCFGWO algorithm draws from both the sine cosine and grey wolf algorithms in an adaptable framework that uses both algorithms' strengths. The experimental results show that the BCM-CNN as a classifier achieved the best results due to the enhancement of the CNN's performance by the CNN optimization's hyperparameters. The BCM-CNN has achieved 99.98% accuracy with the BRaTS 2021 Task 1 dataset.
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DOI: 10.3390/bioengineering10010018
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