Remove Convolutional Neural Networks Remove Deep Learning Remove Explainable AI
article thumbnail

xECGArch: A Multi-Scale Convolutional Neural Network CNN for Accurate and Interpretable Atrial Fibrillation Detection in ECG Analysis

Marktechpost

Deep learning methods excel in detecting cardiovascular diseases from ECGs, matching or surpassing the diagnostic performance of healthcare professionals. Explainable AI (xAI) methods, such as saliency maps and attention mechanisms, attempt to clarify these models by highlighting key ECG features.

article thumbnail

Deep Learning for Medical Image Analysis: Current Trends and Future Directions

Heartbeat

Deep learning automates and improves medical picture analysis. Convolutional neural networks (CNNs) can learn complicated patterns and features from enormous datasets, emulating the human visual system. Convolutional Neural Networks (CNNs) Deep learning in medical image analysis relies on CNNs.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

A Comprehensive Guide on Deep Learning Engineers

Pickl AI

Summary : Deep Learning engineers specialise in designing, developing, and implementing neural networks to solve complex problems. Introduction Deep Learning engineers are specialised professionals who design, develop, and implement Deep Learning models and algorithms.

article thumbnail

Artificial Neural Network: A Comprehensive Guide

Pickl AI

ReLU is widely used in Deep Learning due to its simplicity and effectiveness in mitigating the vanishing gradient problem. Tanh (Hyperbolic Tangent): This function maps input values to a range between -1 and 1, providing a smooth gradient for learning.

article thumbnail

Neural Network in Machine Learning

Pickl AI

Neural networks come in various forms, each designed for specific tasks: Feedforward Neural Networks (FNNs) : The simplest type, where connections between nodes do not form cycles. The resurgence of neural networks in the 1980s was marked by the development of backpropagation, a method for training multi-layer networks.

article thumbnail

Classification Algorithm in Machine Learning: A Comprehensive Guide

Pickl AI

Deep Learning Deep Learning models, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are becoming increasingly popular for complex classification tasks like image and text classification.

article thumbnail

Where AI is headed in the next 5 years?

Pickl AI

Consequently, inspired by the brain’s structure, neural networks experienced a resurgence and contributed to advancements in image and speech recognition. Big Data and Deep Learning (2010s-2020s): The availability of massive amounts of data and increased computational power led to the rise of Big Data analytics.