Search

Breast cancer (BC) is the most common cancer amongst women that threatens the health of women, initial diagnosis of BC becomes essential. Though there were several means to diagnose BC, the standard way is pathological analysis. Precise diagnosis of BC necessitates experienced histopathologists and needs more effort and time for completing this task. Recently, machine learning (ML) was successfully implemented in text classification, image recognition, and object recognition. With the emergence of computer aided diagnoses (CAD) technology, ML was effectively implemented for BC diagnosis. Histopathological image classification depends on deep learning (DL), particularly convolution neural network (CNN), which frequently needs a large amount of labelled training models, whereas the labelled data was hard to obtain. This study develops an Aquila Optimizer(AO) with Hybrid ResNet-DenseNet Enabled Breast Cancer Classification on Histopathological Images (AOHRD-BC2HI). The proposed AOHRD-BC2HI technique inspects the histopathological images for the diagnosis of breast cancer. To accomplish this, the presented AOHRD-BC2HI technique uses hybridization of Resnet with Densenet (HRD) model for feature extraction. Moreover, the HRD method can be enforced for feature extracting procedure in which the DenseNet (feature value memory by concatenation) and ResNet (refinement of feature value by addition) were interpreted. For BC detection and classification, the DSAE model is utilized. The AO algorithm is exploited to improve the detection performance of DSAE model. The experimental validation of the presented AOHRD-BC2HI approach is tested using benchmark dataset and the results are investigated under distinct measures.Also the proposed model achieved the accuracy of 96%. The comparative result reports the improved performance of the presented AOHRD-BC2HI technique over other recent methods. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is categorized as the most widespread cancer among women throughout the world. The earlier analysis of BC assists to increase the survival rate of the disease. BC diagnosis on histopathology images (HIS) is a tedious process that includes recognizing cancerous regions within the microscopic image of breast tissue. There are various methods to discovering BC on HSI, namely deep learning (DL) based methods, classical image processing techniques, and machine learning (ML) based methods. The major problems in BC diagnosis on HSI are the larger size of images and the high degree of variability in the appearance of tumorous regions. With this motivation, this study develops a computer-aided diagnosis using a white shark optimizer with attention-based deep learning for the breast cancer classification (WSO-ABDLBCC) model. The presented WSO-ABDLBCC technique performs accurate classification the breast cancer using DL techniques. In the WSO-ABDLBCC technique, the Guided filtering (GF) based noise removal is applied to improve the image quality. Next, the Faster SqueezeNet model with WSO-based hyperparameter tuning performs the feature vector generation process. Finally, the classification of histopathological images takes place using attention-based bidirectional long short-term memory (ABiLSTM). A detailed experimental validation of the WSO-ABDLBCC occurs utilizing the benchmark Breakhis database. The proposed model achieved an accuracy of 95.2%. The experimental outcomes portrayed that the WSO-ABDLBCC technique accomplishes improved performance compared to other existing models. [ABSTRACT FROM AUTHOR]

Editorial. Cites the primary feature of vol.8, July-September 2004 issue of the "Critical Care Medicine," journal. Insights on corticosol responsiveness of patients of septic shock by using ACTH stimulation test; Recognition of the importance of using corticosteroids for critical illness due to adrenal insufficiency; Experimental observations of low dose hydrocortisone in patients with sepsis; Absence of confirmation on the effect of combining fludrocortisone with corticosteroids for treatment.