Alzheimer’s Disease Stage Classification Using a Deep Transfer Learning and Sparse Auto Encoder Method

Alzheimer’s Disease(AD)is a progressive neurological disease.Early diagnosis of this illness using conventional methods is very challenging.Deep Learning(DL)is one of the finest solutions for improving diagnostic procedures’performance and forecast accuracy.The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups.In light of research investigations,it is vital to consider age as one of the key criteria when choosing the subjects.The younger subjects are more susceptible to the perishable side than the older onset.The proposed investigation concentrated on the younger onset.The research used deep learning models and neuroimages to diagnose and categorize the disease at its early stages automatically.The proposed work is executed in three steps.The 3D input images must first undergo image pre-processing using Weiner filtering and Contrast Limited Adaptive Histogram Equalization(CLAHE)methods.The Transfer Learning(TL)models extract features,which are subsequently compressed using cascaded Auto Encoders(AE).The final phase entails using a Deep Neural Network(DNN)to classify the phases of AD.The model was trained and tested to classify the five stages of AD.The ensemble ResNet-18 and sparse autoencoder with DNN model achieved an accuracy of 98.54%.The method is compared to state-of-the-art approaches to validate its efficacy and performance.

Influence of GABAergic Pathway on Retinal Adaptation-Related Response Changes

Retinal ganglion cells(RGCs) exhibit adaptive changes in response to sustained light stimulation,which include decrease in firing rate, tendency to shrink in receptive field(RF) size and reduction in synchronized activities. Gamma-aminobutyric acid-ergic(GABAergic) pathway is an important inhibitory pathway in retina.In the present study, the effects of GABAergic pathway on the contrast adaptation process of bullfrog RGCs were studied using multi-electrode recording technique. It was found that the application of bicuculline(BIC), a gamma-aminobutyric acid A(GABAA) receptor antagonist, caused a number of changes in the RGCs' response characteristics, including attenuation in adaptation-dependent firing rate decrease and the adaptation-dependent weakening in synchronized activities between adjacent neuron-pairs, whereas intensified the adaptation-dependent RF size shrinkage. These results suggest that GABAAreceptors are involved in the modulation of the firing activity and synchronized activities in contrast adaptation process of the RGCs, whereas the adaptation-related RF property changes involve more complicated mechanisms.

Curvelet transform and contrast adaptive clip histogram equalization-based image defogging algorithm

Due to the scattering effect of suspended particles in the atmosphere, foggy day images have reduced visibility and contrast significantly. Considering the loss of details and uneven defogging results of the contrast limited adaptive histogram equalization （CLAHE） algorithm, a curvelet transform and contrast adaptive clip histogram equalization （HE）-based foggy day image enhancement algorithm is proposed. The proposed algorithm transforms an image to the curvelet domain and enhances the image detail information via a nonlinear transformation of high frequency curvelet coefficients. After curvelet reconstruction, the contrast adaptive clip HE method is adopted to enhance the total image contrast and the foggy day image contrast and detail information. During the histogram clipping process, the clip limit value is adaptively selected based on image contrast and the sub-block image histogram variance. A comparative analysis of the foggy day image enhancement results are obtained by applying CLAHE, and some classical single image defogging algorithms and the proposed algorithm are also conducted to prove the effectiveness of the proposed algorithm with objective parameters.

Crack identification method of highway tunnel based on image processing

In this paper,the images of tunnel surface are obtained by tunnel lining rapid inspection system,and tunnel crack forest dataset(TCFD)is established.The disaster characteristics of tunnel cracks are analyzed and summarized.Solutions of tunnel crack segmentation(TCS)method are developed for the detection and recognition of cracks on tunnel lining.According to the image features of the tunnel lining and the optical principal of detection equipment,effective image pre-processing steps are carried out before crack extraction.The tunnel image of TCFD is divided into appropriate number of blocks to magnify the local features of tunnel cracks.Local threshold segmentation method is used to traverse the blocks successively,and the first target block with crack is obtained.The seed in the target block were obtained by adaptive localization method and mapped to the whole image.Region growing is performed through crack seed until complete tunnel crack is extracted.The results show that the precision,recall rate and F-measure of tunnel cracks under the TCS method can reach 92.58%,93.07%and 92.82%without strong interference.According to the binary images processed by TCS method,the projection images of different types of tunnel cracks and their respective laws are obtained.Furthermore,the TCS method is implemented and deployed as a GUI software application.