EpiBrCan-Lite: A lightweight deep learning model for breast cancer subtype classification using epigenomic data.

Background And Objectives: Early breast cancer subtypes classification improves the survival rate as it facilitates prognosis of the patient. In literature this problem was prominently solved by various Machine Learning and Deep Learning techniques. However, these studies have three major shortcomings: huge Trainable Weight Parameters (TWP), suffer from low performance and class imbalance problem.

Dermoscopic Perspective of Pityriasis Versicolor in a Cross-Sectional Study.

Background: Pityriasis versicolor is a common superficial fungal infection which is usually easily diagnosed with Wood's lamp examination and 10% potassium hydroxide mount. However, these modalities have varying sensitivity and specificity.

Aims And Objectives: This study aimed to ascertain the dermoscopic features of pityriasis versicolor lesionally as well as perilesionally using dermoscopy, a non-invasive diagnostic tool.

PDSE-Lite: lightweight framework for plant disease severity estimation based on Convolutional Autoencoder and Few-Shot Learning.

Plant disease diagnosis with estimation of disease severity at early stages still remains a significant research challenge in agriculture. It is helpful in diagnosing plant diseases at the earliest so that timely action can be taken for curing the disease. Existing studies often rely on labor-intensive manually annotated large datasets for disease severity estimation.

TrIncNet: a lightweight vision transformer network for identification of plant diseases.

In the agricultural sector, identifying plant diseases at their earliest possible stage of infestation still remains a huge challenge with respect to the maximization of crop production and farmers' income. In recent years, advanced computer vision techniques like Vision Transformers (ViTs) are being successfully applied to identify plant diseases automatically. However, the MLP module in existing ViTs is computationally expensive as well as inefficient in extracting promising features from diseased images.