A Feature Extraction Using Probabilistic Neural Network and BTFSC-Net Model with Deep Learning for Brain Tumor Classification

Abstract

Background and Objectives: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. Materials and Methods: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images’ slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. Results: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. Conclusions: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance.

Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization

“…Here, 'n' refers to the number of filters, 'sf ' denotes the size of filters, 'sp' represents the size of the max pooling layer, and 'l' indicates the step size. Several other configurations, such as [8,7,4,3] and [9,8,2,4], also yielded high accuracies above 97%. However, certain configurations, e.g., [1,5,2,3] and [1,5,2,2], resulted in significantly lower accuracies of 84.20% and 50%, respectively.…”

“…The best accuracy obtained was 97.12% with the hyper-parameter values [n, s f , sp, l] = [12,5,3,2]. Configurations such as [8,5,3,4] and [12,7,4, 2] also yielded accuracies above 96%. However, the [1, 5, 2, 2] and [5,5,2,2] configurations resulted in lower accuracies of 82.40% and 60.53%, respectively.…”

“…In the ADNI dataset, the PSO-CNN model demonstrated promising performance in Alzheimer's disease prediction. The best accuracy of 98.50% was achieved with the hyper-parameter values [n, s f , sp, l] = [12,8,4,3]. Here, 'n' refers to the number of filters, 'sf ' denotes the size of filters, 'sp' represents the size of the max pooling layer, and 'l' indicates the step size.…”

“…Magnetic resonance imaging (MRI), a type of brain imaging that allows for the representation of the structure and functionality of the brain, has been used in the healthcare diagnosis of brain conditions. Medical professionals assess the symptoms and signs of AD and brain tumors [8]. Doctors may request additional laboratory examinations, brain imaging exams, or memory tests for patients.…”

“…Based on the effective performance of DNN and CNN methods in various image classification tasks [8,10], the study goal is to help design a more efficient system for making a highly accurate prediction for AD and brain tumor diseases using a highly efficient optimization algorithm known as Particle Swarm Optimization (PSO). Our main contributions can be recapped as follows:…”