Pathology

Intelligent Diagnosis Method for New Diseases Based on Fuzzy SVM Incremental Learning.

The diagnosis of new diseases is a challenging problem. In the early stage of the emergence of new diseases, there are few case samples; this may lead to the low accuracy of intelligent diagnosis. Because of the advantages of support vector machine (SVM) in dealing with small sample problems, it is selected for the intelligent diagnosis method. The standard SVM diagnosis model updating needs to re...

Hybrid Convolution Neural Network in Classification of Cancer in Histopathology Images.

Cancer statistics in 2020 reveals that breast cancer is the most common form of cancer among women in India. One in 28 women is likely to develop breast cancer during their lifetime. The mortality rate is 1.6 to 1.7 times higher than maternal mortality rates. According to the US statistics, about 42,170 women in the US are expected to die in 2020 from breast cancer. The chance of survival can be i...

Computational Intelligence Method for Detection of White Blood Cells Using Hybrid of Convolutional Deep Learning and SIFT.

Infection diseases are among the top global issues with negative impacts on health, economy, and society as a whole. One of the most effective ways to...

Nerve spare robot assisted laparoscopic prostatectomy with amniotic membranes: medium term outcomes.

dHACM is a source of factors including cytokines that allow anti-inflammatory and proliferative elements to be utilized for wound and ulcer management...

Reduced-Dose Deep Learning Reconstruction for Abdominal CT of Liver Metastases.

Background Assessment of liver lesions is constrained as CT radiation doses are lowered; evidence suggests deep learning reconstructions mitigate such...

Classifying microscopic images as acute lymphoblastic leukemia by Resnet ensemble model and Taguchi method.

BACKGROUND: Researchers have attempted to apply deep learning methods of artificial intelligence for rapidly and accurately detecting acute lymphoblas...

Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular e...

Grade classification of human glioma using a convolutional neural network based on mid-infrared spectroscopy mapping.

This study proposes a convolutional neural network (CNN)-based computer-aided diagnosis (CAD) system for the grade classification of human glioma by u...

Non-invasive scoring of cellular atypia in keratinocyte cancers in 3D LC-OCT images using Deep Learning.

Diagnosis based on histopathology for skin cancer detection is today's gold standard and relies on the presence or absence of biomarkers and cellular ...

Multimodal Imaging of Target Detection Algorithm under Artificial Intelligence in the Diagnosis of Early Breast Cancer.

This study aimed to analyze the diagnostic value of multimodal images based on artificial intelligence target detection algorithms for early breast ca...

Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model.

This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive c...

Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning.

Signet ring cell carcinoma (SRCC) is a malignant tumor of the digestive system. This tumor has long been considered to be poorly differentiated and hi...

Computed Tomography Image Features under Deep Learning Algorithm Applied in Staging Diagnosis of Bladder Cancer and Detection on Ceramide Glycosylation.

The research is aimed at investigating computed tomography (CT) image based on deep learning algorithm and the application value of ceramide glycosyla...

AI-based carcinoma detection and classification using histopathological images: A systematic review.

Histopathological image analysis is the gold standard to diagnose cancer. Carcinoma is a subtype of cancer that constitutes more than 80% of all cance...

Gibberellic acid-induced hepatorenal dysfunction and oxidative stress: Mitigation by quercetin through modulation of antioxidant, anti-inflammatory, and antiapoptotic activities.

The plant growth regulator gibberellic acid (GA3) is widely used in agriculture in many countries. However, little is known about its danger to human ...

Real-Time Ultrasound Doppler Tracking and Autonomous Navigation of a Miniature Helical Robot for Accelerating Thrombolysis in Dynamic Blood Flow.

Untethered small-scale robots offer great promise for medical applications in complex biological environments. However, challenges remain in the contr...

Minimally invasive hysterectomy for benign indications-surgical volume matters: a retrospective cohort study comparing complications of robotic-assisted and conventional laparoscopic hysterectomies.

The objective of this study was to evaluate the incidence of perioperative complications in robotic-assisted hysterectomies performed by high-volume r...

Application of Deep Learning in Lung Cancer Imaging Diagnosis.

Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mai...

Deep transfer learning based model for colorectal cancer histopathology segmentation: A comparative study of deep pre-trained models.

Colorectal cancer is one of the leading causes of cancer-related death, worldwide. Early detection of suspicious tissues can significantly improve the...

Aquoeus Extracts of Daucus Carota (Linn) Protected the Postnatal Developing Cerebellum of Wistar Rats Against Arsenic-Induced Oxidative Stress.

The neuroprotective effects of the aqueous extract of Daucus carota (Dc) tuber against arsenic-induced oxidative damage on the developing cerebellum o...