AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
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.
Breast cancer is one of the leading causes of cancer-related mortality worldwide, and despite advances in clinical diagnosis, challenges such as inconclusive imaging results and inter-observer variability highlight the need for complementary computational approaches to support early and accurate detection. In this stud...
The proposed HrybridViT-CAM, a hybrid deep learning system that integrates convolution neural networks, Vision Transformers, and multi-scale attention system in order to classify breast cancer using histopathology images was able to detect the malignant regions of interest (ROIs) like nuclei pleomorphism, atypia chroma...
An attention-based CNN model for classifying ovarian cancer that outperforms existing methods based on quantitative evaluation is proposed and incorporated into a web application utilizing the FastAPI framework to facilitate real-time predictions.
Lung cancer is a major cause of cancer-related mortality, and accurate classification of histopathological tissue patterns can support the analysis of lung cancer subtypes. Manual examination of histopathological images is time-consuming and requires careful assessment of cellular and tissue-level morphological pattern...
Breast cancer remains the most frequently diagnosed cancer and a leading cause of cancer death among women worldwide, so tools that support early and accurate detection are urgently needed. This review synthesizes a pool of one hundred studies, largely published between 2023 and 2026, on deep learning, ensemble learnin...
Purpose: This study aims to compare three transfer learning architectures and develop an ensemble learning approach for breast cancer classification in ultrasound images. The objective of this study is to compare the three transfer learning architectures and evaluate an ensemble learning approach to determine the best...
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