Brain Image Classification Using Time Frequency Extraction with Histogram Intensity Similarity

Abstract

Brain medical image classification is an essential procedure in Computer-Aided Diagnosis (CAD) systems. Conventional methods depend specifically on the local or global features. Several fusion methods have also been developed, most of which are problem-distinct and have shown to be highly favorable in medical images. However, intensity-specific images are not extracted. The recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images, compromising normalization. To solve these classification problems, in this paper, Histogram and Time-frequency Differential Deep (HTF-DD) method for medical image classification using Brain Magnetic Resonance Image (MRI) is presented. The construction of the proposed method involves the following steps. First, a deep Convolutional Neural Network (CNN) is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for extraction. Second, a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency maps. Finally, an efficient model that is based on Differential Deep Learning is designed for obtaining different classes. The proposed model is evaluated using National Biomedical Imaging Archive (NBIA) images and validation of computational time, computational overhead and classification accuracy for varied Brain MRI has been done.

Indoor Scene Splicing Based on Genetic Algorithm and ORB

“…ORB algorithm [13][14][15] is an improved algorithm based on the fusion between FAST [13] feature point detection and BRIEF [14] feature description. The algorithm obtains the feature information of the image, uses the binary string to describe the feature information, and maintains invariance to changes in target translation, rotation, viewing angle and illumination.…”

“…The principle [15,16] of image feature point matching is to select a feature point in the reference image, and then search for feature points in the image to be matched according to a certain similarity measurement criterion. If the two feature points meet the criterion, these two points are considered to be matched.…”

Enhancing Brain Segmentation in MRI through Integration of Hidden Markov Random Field Model and Whale Optimization Algorithm

“…For example, Lee B et al [21] introduced a nonoverlapping patch-wise U-net architecture to remedy the drawbacks of the conventional U-Net with greater retention of local information. On the other hand, Renukadevi [22] et al proposed a medical image classification with a Histogram and Time-Frequency Differential Deep Learning method using brain Magnetic Resonance Imaging. First, a supervised training method was applied by an intensity-oriented Histogram to prepare the feature extraction step.…”