SwinDFU-Net: Deep learning transformer network for infection identification in diabetic foot ulcer.

Background: The identification of infection in diabetic foot ulcers (DFUs) is challenging due to variability within classes, visual similarity between classes, reduced contrast with healthy skin, and presence of artifacts. Existing studies focus on visual characteristics and tissue classification rather than infection detection, critical for assessing DFUs and predicting amputation risk.

Objective: To address these challenges, this study proposes a deep learning model using a hybrid CNN and Swin Transformer architecture for infection classification in DFU images.

Content-Based Image Retrieval Using Colour, Gray, Advanced Texture, Shape Features, and Random Forest Classifier with Optimized Particle Swarm Optimization.

In this paper, a new approach for Content-Based Image Retrieval (CBIR) has been addressed by extracting colour, gray, advanced texture, and shape features for input query images. Contour-based shape feature extraction methods and image moment extraction techniques are used to extract the shape features and shape invariant features. The informative features are selected from extracted features and combined colour, gray, texture, and shape features by using PSO.

Automated Detection of Infection in Diabetic Foot Ulcer Images Using Convolutional Neural Network.

A bacterial or bone infection in the feet causes diabetic foot infection (DFI), which results in reddish skin in the wound and surrounding area. DFI is the most prevalent and dangerous type of diabetic mellitus. It will mainly occur in people with heart disease, renal illness, or eye disease.

Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model.

Wireless capsule endoscopy is a noninvasive wireless imaging technology that becomes increasingly popular in recent years. One of the major drawbacks of this technology is that it generates a large number of photos that must be analyzed by medical personnel, which takes time. Various research groups have proposed different image processing and machine learning techniques to classify gastrointestinal tract diseases in recent years.

Analysis of DNA Sequence Classification Using CNN and Hybrid Models.

In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19.

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images.

Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years.