Brain Tumor Detection and Classification Using Deep Learning and Sine-Cosine Fitness Grey Wolf Optimization
ABSTARCT :
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.
EXISTING SYSTEM :
a method that detects if there is a tumor or not and then classifies the tumor type. The proposed method was tested on 150 T1-weighted MRI brain imaging for identifying brain tumors. The supervised approach was utilized for the classification process, and the principal component analysis was employed for feature extraction.
They also assessed the tumor’s area and volume to determine the tumor’s levels. The findings of the experiments demonstrate that KSVM is 97 percent accurate in classifying brain tumors.
DISADVANTAGE :
Acquiring large and diverse datasets of medical images with annotated tumors can be challenging. Limited data can lead to models that are not sufficiently generalized.
Class imbalance between tumor and non-tumor cases can affect model performance, requiring techniques like data augmentation or sampling strategies.
Deep learning models, especially complex architectures like CNNs, can be difficult to interpret. Understanding how and why a model makes a particular prediction (e.g., identifying tumor regions) is crucial for medical acceptance and trust.
Lack of interpretability can hinder clinical adoption as healthcare professionals may be reluctant to trust "black-box" models without understanding their decision-making process.
PROPOSED SYSTEM :
This section proposes a Brain Tumor Classification Model (BCM-CNN) based on an advanced model using a Convolutional Neural Network. The overall architecture of the proposed model . The BCM-CNN is used to diagnose a brain tumor.
It consists of a hyperparameters optimization, followed by an Inception-ResnetV2 training model. The model’s output is a binary 0 or 1 (0: Normal, 1: Tumor) and uses common pre-trained models (Inception-ResnetV2) to enhance the brain tumor diagnosis process.
ADVANTAGE :
The data augmentation technique is used in this study to artificially generate fresh training data from the current data. As a sort of data augmentation, picture augmentation produces altered representations of the training dataset’s images.
The input dataset is subjected to several image transformations, such as horizontal and vertical shift, horizontal and vertical flip, random rotation, and random zoom. The shift augmentation maintains the same image dimensions while shifting all of the MRI image’s pixels in either a horizontal or vertical direction.
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