AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Improved robustness and explainability in lesion classification is demonstrated, supporting the applicability of the proposed method to real clinical settings and future research focuses on multimodal fusion and lightweight deployment on edge devices.
A comprehensive deep learning-based framework that leverages three benchmark datasets—PH2, ISIC (Benign vs Malignant), and HAM10000—using transfer learning and ensemble techniques is presented, demonstrating a notable increase in classification accuracy.
The timely and precise diagnosis of colorectal cancer is a critical area for enhancing the quality of care for patients, which could lead to better outcomes. Despite the fact that deep learning has demonstrated great potential in the field of histopathological image analysis, the accuracy of models can be influenced by...
This research explores deep learning methods, specifically using ResNet architectures, combined with various optimization methods, including Adam, Stochastic Gradient Descent with Momentum (SGDM), and Root Mean Square Propagation, for classifying colorectal cancer types from histological images.
The results demonstrate that deep learning techniques can significantly assist in early detection and classification of skin cancer, thereby supporting dermatologists in clinical decision-making and improving diagnostic efficiency and mortality rates associated with skin cancer.
A robust soft-voting ensemble-based deep learning model for automatic binary breast cancer identification using histopathology images can achieve effective classification performance without excessive attention complexity while keeping clear visual evidence.
The most important contributions from this review are a quantitative comparison of efficiency for edge versus cloud deployment, detection of dataset bias, and practical recommendations regarding infrastructure, regulatory pathways, and privacy-preserving federated learning.
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