Machine Learning

It's Time to Start Your Journey to Machine Learning

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data patterns and make predictions or decisions without being explicitly programmed. It relies on algorithms that can analyze and identify patterns in large datasets, improving their accuracy over time as they are exposed to more data. Essentially, ML models “learn” from previous experiences, allowing them to generalize and apply their knowledge to new situations.

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The flowchart of the medical image classification with SAM-based Image Enhancement (SAM-IE). The terms ‘low-grade’ and ‘high-grade’ can refer to benign and malignant, respectively, or to different degrees of disease severity.

SAM-IE: Enhancing Medical Imaging for Disease Detection

Medical imaging is a cornerstone of modern diagnostics, yet clinicians often grapple with challenges like ambiguous anatomical structures, inconsistent...

Latest Trends in Machine Learning

New Horizon in AI: Machine learning (ML) is evolving at an unprecedented pace, driving innovations across industries and redefining the boundaries of technology....

Illustration of the framework of the proposed method. In the first stage, an adversarial image is processed with multiscale analysis: the image will be downsampled by a factor of 1/2 and 1/4, respectively, and upsampled by a factor of 2. Then in the second stage, we design and insert 𝑁 diffusive and denoising aggregation mechanism (DDA) blocks sequentially. Each DDA block involves a diffusive process (Section 3.2), a denoising process (Section 3.3), and an aggregation process (Section 3.4). The output samples from the last DDA block will be inversely processed to the original scale and smoothed to obtain the reversed image.

Skin Cancer AI Combats Adversarial Attacks with MDDA

In recent years, deep learning has revolutionized dermatology by automating skin cancer diagnosis with impressive accuracy. AI-powered systems like convolutional...

The proposed multi-label skin lesion classification framework has three branches: dermoscopy imaging modality branch (green block), clinical imaging modality branch (yellow block), and a hybrid-meta branch (orange block). Modified Xception module based dermoscopy and clinical imaging modalities’ features are first concatenated, then fed to the input of hybrid-meta branch, and finally concatenated with the meta-data.

AI Revolutionizes Skin Cancer Diagnosis

New Deep Learning Model Boosts Accuracy for Early Detection Skin cancer, particularly melanoma, remains one of the deadliest cancers worldwide. The stakes...

Schematic representation of the proposed LungCT-NET, incorporating preprocessing, reconstructed transfer learning (TL) models, stacking ensemble learning, SHAP (Shapley Additive Explanations) for explainable artificial intelligence (XAI), along with model evaluation and comparison.

LungCT-NET: Revolutionizing Lung Cancer Diagnosis with AI

Introduction: The Urgent Need for Early Lung Cancer Detection Lung cancer is the leading cause of cancer-related deaths worldwide, accounting for 1.8 million...

Revolutionizing Brain Tumor Diagnosis: GATE-CNN

For patients facing a potential brain tumor diagnosis, time is brain tissue. Early and accurate detection isn’t just beneficial; it’s often...

The block diagram of the Brain-GCN-Net model for Brain Tumor Diagnosis.

Beyond the Naked Eye: How AI Fusion is Revolutionizing Brain Tumor Diagnosis

Every year, thousands face the daunting diagnosis of a brain tumor. Speed and accuracy are paramount – early detection significantly improves survival...

Block diagram of the proposed Res-WG-KNN model for pneumonia prediction comprising two sub-models, and soft voting ensemble learning. RFC represents Regularized Fully Connected Layers, FV represents Feature Vector, and D represents Dimension. Pneumonia and Non-Pneumonia represented by subscripts p and n respectively.

AI MODEL Boosts Pneumonia Detection in Chest X-Rays

Pneumonia remains a leading cause of global mortality, particularly among children and the elderly. Early detection is critical for improving survival...

complete overview of our proposed model for brain tunor classification

Revolutionizing Brain Tumor Classification: The Power of DEF-SwinE2NET

Brain tumors are among the most challenging medical conditions to diagnose and treat. Their complexity, coupled with the need for precise classification,...

A biopsy image of a complex wound analsis by AI, showing segmented tissue types like epidermis, dermis, and necrosis.

Revolutionizing Wound Care: How AI is Transforming Complex Wound Analysis

Chronic wounds affect millions of people worldwide, causing pain, disability, and staggering healthcare costs. According to the Wound Healing Society,...

Sam2Rad architecture The Sam2Rad architecture incorporates a (hierarchical) two-way attention module to predict prompts for queried objects. Each object/class is represented by learnable queries . The Prompt Predictor Network (PPN) predicts bounding box coordinates of the target object , an intermediate mask prompt , and high-dimensional prompt embeddings . The prompt embeddings can represent various prompts suitable for the task, such as several point prompts or high-level semantic information. The predicted prompts (i.e., , , & ) are then fed to SAM’s mask decoder to generate the final segmentation mask. PPN also supports multi-class medical image segmentation by using class-specific queries .

Sam2Rad: Revolutionizing Medical Image Segmentation with AI-Powered Automation

Medical imaging has long been a cornerstone of modern healthcare, enabling clinicians to diagnose, treat, and monitor a wide range of conditions. However,...

Fig. 1. The overall framework of our multi-teacher distillation method.

Adaptive Multi-Teacher Knowledge Distillation for Segmentation

Medical image segmentation is a cornerstone of modern diagnostics, enabling precise identification of tumors, organs, and anomalies in MRI and CT scans....

3DL-Net’s three-stage architecture: preliminary segmentation, multi-scale context extraction, and dendritic refinement for precise medical image analysis.

Revolutionizing Medical Image Segmentation with 3DL-Net: A Breakthrough in Global–Local Feature Representation

Medical image segmentation is a cornerstone of modern healthcare, enabling precise delineation of anatomical structures and pathological regions. From...

Advances in Attention Mechanisms for Medical Image Segmentation: A Comprehensive Guide

Medical image segmentation is a cornerstone of modern healthcare, enabling precise diagnosis and treatment planning through advanced imaging technologies....

Medical Image Segmentation with Med-SA: Adapting SAM for Healthcare

Medical image segmentation is a cornerstone of modern healthcare diagnostics, enabling precise identification and analysis of organs, tissues, and abnormalities....