Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.

Problem: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques.

Exploring Gamification and Serious Games for Mental Health: A Narrative Review.

The development of serious video games using gamification techniques and applying them to the management of mental health problems has emerged as one of the significant innovations in technology and mental health. However, various issues exist from the design stage of video games to the implementation stage, which can lead to problems with usability and accessibility and have non-beneficial effects on the individual. This review article provides an overview of various gamification technologies currently used in video games and virtual reality-based video games.

Assessment of Self-Medication Practices and Knowledge Among Medical Students in Chengalpattu District: A Cross-Sectional Study.

Introduction: The practice of self-medication is wherein individuals initiate the use of medications without consulting a healthcare professional. College life is a period marked by academic, social, and personal changes. Due to their greater freedom and the pressure of academic success, students face various health issues.

An optimal deep learning approach for breast cancer detection and classification with pre-trained CNN-based feature learning mechanism.

Breast cancer (BC) is the most dominant kind of cancer, which grows continuously and serves as the second highest cause of death for women worldwide. Early BC prediction helps decrease the BC mortality rate and improve treatment plans. Ultrasound is a popular and widely used imaging technique to detect BC at an earlier stage.

Contribution of piezoelectric effect on piezo-phototronic coupling in ferroelectrics: A theory assisted experimental approach on NBT.

A new study explores the distinct roles of spontaneous polarization and piezoelectric polarization in piezo-phototronic coupling. This investigation focuses on differences in photocatalytic and piezo-photocatalytic performance using sodium bismuth titanate, a key ferroelectric material. The research aims to identify which type of polarization has a greater influence on piezo-phototronic effects.