Skin cancer segmentation and recognition from dermoscopy images: a novel framework based on improved DeepLabV3+ and network-level fused deep architectures.

Introduction: Skin lesion segmentation and classification is an active research area in medical imaging for the large number of reported deaths in the recent years. Early diagnosis of skin cancer is essential to decrease the death rate and increase life expectancy.

Objectives: Several artificial intelligence (AI) based techniques have been introduced in the literature for the diagnosing skin cancer; however, due to challenge of imbalanced datasets, irregular lesion shape, presence of lesions on boundary regions, and selection of inappropriate model selection, the performance of AI model is highly impacted.

Multiclass skin lesion classification and localziation from dermoscopic images using a novel network-level fused deep architecture and explainable artificial intelligence.

Background And Objective: Early detection and classification of skin cancer are critical for improving patient outcomes. Dermoscopic image analysis using Computer-Aided Diagnostics (CAD) is a powerful tool to assist dermatologists in identifying and classifying skin lesions. Traditional machine learning models require extensive feature engineering, which is time-consuming and less effective in handling complex data like skin lesions.

Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review.

Numerous individuals encounter challenges in verbal communication due to various factors, including physical disabilities, neurological disorders, and strokes. In response to this pressing need, technology has actively pursued solutions to bridge the communication gap, recognizing the inherent difficulties faced in verbal communication, particularly in contexts where traditional methods may be inadequate. Electroencephalogram (EEG) has emerged as a primary non-invasive method for measuring brain activity, offering valuable insights from a cognitive neurodevelopmental perspective.

A Computer-Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification.

In the USA, each year, almost 5.4 million people are diagnosed with skin cancer. Melanoma is one of the most dangerous types of skin cancer, and its survival rate is 5%.