Visual-Saliency-Based Abnormality Detection for MRI Brain Images—Alzheimer’s Disease Analysis

Abstract

In recent years, medical image analysis has played a vital role in detecting diseases in their early stages. Medical images are rapidly becoming available for various applications to solve human problems. Therefore, complex medical features are needed to develop a diagnostic system for physicians to provide better treatment. Traditional methods of abnormality detection suffer from misidentification of abnormal regions in the given data. Visual-saliency detection methods are used to locate abnormalities to improve the accuracy of the proposed work. This study explores the role of a visual saliency map in the classification of Alzheimer’s disease (AD). Bottom-up saliency corresponds to image features, whereas top-down saliency uses domain knowledge in magnetic resonance imaging (MRI) brain images. The novelty of the proposed method lies in the use of an elliptical local binary pattern descriptor for low-level MRI characterization. Ellipse-like topologies help to obtain feature information from different orientations. Extensively directional features at different orientations cover the micro patterns. The brain regions of the Alzheimer’s disease stages were classified from the saliency maps. Multiple-kernel learning (MKL) and simple and efficient MKL (SEMKL) were used to classify Alzheimer’s disease from normal controls. The proposed method used the OASIS dataset and experimental results were compared with eight state-of-the-art methods. The proposed visual saliency-based abnormality detection produces reliable results in terms of accuracy, sensitivity, specificity, and f-measure.

An effective model for the iris regional characteristics and classification using deep learning alex network

“…The centre pixel location was identified using Equations ( 15) and (19). The diameter and radius of the iris boundary were computed using Equations ( 20) and (21). The corresponding results are shown in Figure 5j.…”

“…The authors also developed a multiple left-right point (MLRP) algorithm for detecting pixels in the iris boundary using the visible iris region [18]. There have been many studies applying deep learning, such as CNN to classification for disease diagnosis [19][20][21]. Matten et al [22] proposed diabetic retinopathy (DR) detection using a CNN.…”

Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform

“…Radiologists can make diagnostic decisions based on the information provided by the system. Some machine learning and deep learning techniques have also been used by Bruntha et al 13 and Andrushia et al 14 for image classification to aid in the early detection of diseases.…”

Measurement Model for Medical Image Feature Matrix Similarity Based on CNN

“…Following the above process, feature points of medical images are extracted orderly, and the feature points in the images are integrated and stored in the medical image database [14].…”