Breast cancer continues to be a predominant cause of cancer-related fatalities among women worldwide. Timely and precise diagnosis is essential for successful intervention and enhanced patient outcomes. Artificial intelligence (AI), especially deep learning (DL) methodologies, is swiftly revolutionizing breast cancer diagnostics, providing unparalleled prospects to improve the accuracy and efficacy of detection and characterization. This editorial paper explores the crucial role of AI in breast cancer imaging, analyzing its utilization in computer-aided diagnosis (CAD) and its capacity to address the intrinsic limits of manual assessment. The article will examine several DL approaches uti... More >

The early identification of Amyotrophic Lateral Sclerosis (ALS), a progressive neurological disease, using blood-based transcriptome biomarker is gaining attention. The classification of ALS from blood transcriptomic data remains challenging due to class imbalance and high dimensionality. This extension of a previous study that utilized machine learning on the microarray dataset includes a synthetic data augmentation method employing the Synthetic Minority Over-sampling Technique (SMOTE) to improve classification accuracy. Following the use of Fisher Score, t-test, PCA, and Ant Colony Optimization for feature selection, SMOTE was employed to produce synthetic ALS samples and to imbalance the... More >