An Effective and Novel Approach for Brain Tumor Classification Using AlexNet CNN Feature Extractor and Multiple Eminent Machine Learning Classifiers in MRIs

Abstract

A brain tumor is an uncontrolled malignant cell growth in the brain, which is denoted as one of the deadliest types of cancer in people of all ages. Early detection of brain tumors is needed to get proper and accurate treatment. Recently, deep learning technology has attained much attraction to the physicians for the diagnosis and treatment of brain tumors. This research presents a novel and effective brain tumor classification approach from MRIs utilizing AlexNet CNN for separating the dataset into training and test data along with extracting the features. The extracted features are then fed to BayesNet, sequential minimal optimization (SMO), Naïve Bayes (NB), and random forest (RF) classifiers for classifying brain tumors as no-tumor, glioma, meningioma, and pituitary tumors. To evaluate our model’s performance, we have utilized a publicly available Kaggle dataset. This paper demonstrates ROC, PRC, and cost curves for realizing classification performance of the models; also, performance evaluating parameters, such as accuracy, sensitivity, specificity, false positive rate, false negative rate, precision, f-measure, kappa statistics, MCC, ROC area, and PRC area, have been calculated for four testing options: the test data itself, cross-validation fold (CVF) 4, CVF 10, and percentage split (PS) 34% of the test data. We have achieved 88.75%, 98.15%, 86.25% and 100% of accuracy using the AlexNet CNN+BayesNet, AlexNet CNN+SMO, AlexNet CNN+NB, and AlexNet CNN+RF models, respectively, for the test data itself. The results imply that our approach is outstanding and very effective.

Performance Analysis of Brain Tumor Classification on MRI images using Pretrained Deep Learning Models

“…In the study by Aloka Sarkar [14], a pioneering and efficient strategy for brain tumor classification is presented, utilizing the AlexNet-CNN feature extractor and multiple prominent machine learning classifiers applied to MRI data. The methodology involves the initial extraction of features using the AlexNet CNN deep learning model.…”

Deep Feature Extraction with Cubic-SVM for Classification of Brain Tumor

“…The brain tumor classification of MR images can be done by feeding the image features to machine learning classification algorithms. Several pre-trained CNN models have been used in brain tumor recognition such as fficientNetB0 [17]- [20], VGG-19 [18], [19], [21], VGG-16 [2], [22], [23], ResNet [2], [18], [19], AlexNet [2], [24], [25], SqeezNet [2],…”

“…McIntyre andTuba[30], They proposed a technique involving segmentation of brain MRI images using k-means clustering algorithm and image pre-processing. The gray level cooccurrence matrix is used to extract texture features from the region of interest, Studies in Engineering and Exact Sciences, Curitiba, v.5, n.1, p [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. 2024 and then these features are used to train a support vector machine for classification, accuracy rate of 95.21%.…”

Review of AlexNet for Medical Image Classification

“…For example, Ramya, et al [78] proposed MIDNet18 CNN, which is more profound than AlexNet, and experimentally proved that this model performs better than AlexNet on specific brain tumor image datasets. Deeper layers allow the CNN to extract richer features, and AlexNet does not serve as well as deeper CNNs on some datasets due to its shallow layers.AlexNet can be used as a feature extractor with several well-known machine-learning classifiers to improve brain tumor image classification [79].…”