Adnan Saeed - AI Trend Blend

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 Was Never Built for Hospitals, So Researchers Made It Useful Anyway

1 Comment / Machine Learning, Medical AI / Adnan Saeed

Analysis by the aitrendblend editorial team. Medical review by . Twelve minute read. Source paper published in Expert Systems With Applications, March 2024. Segment Anything Model Medical Image Classification ResNet50 Swin Transformer Breast Ultrasound Fundus Imaging Foundation Models A radiologist looking at a breast ultrasound scan does not see pixels. She sees a mass, its […]

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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

Leave a Comment / Machine Learning / Adnan Saeed

Pneumonia remains a leading cause of global mortality, particularly among children and the elderly. Early detection is critical for improving survival rates, but traditional diagnostic methods rely heavily on chest X-rays (CXRs), which can be subjective and time-consuming for radiologists. Even subtle abnormalities in X-rays—such as lung opacities or fluid buildup—are often imperceptible to the

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Fig. 8. The training process of the classification and grading of cardiac views.

CACTUS Dataset Teaches AI to Grade Cardiac Ultrasound Quality

2 Comments / Machine Learning / Adnan Saeed

Analysis by the aitrendblend editorial team AI for medical imaging and healthcare 12 min read CACTUS: A phantom scanned cardiac ultrasound frame feeding a shared encoder that both names the view and grades its quality. A trainee holds an ultrasound probe against a mannequin chest in a Concordia University lab, angling it a few millimeters

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complete overview of our proposed model for brain tunor classification

DEF-SwinE2NET Adds Two Small Modules to EfficientNetV2S and Nearly Erases Its Errors

1 Comment / Machine Learning, Medical AI / Adnan Saeed

Analysis by the aitrendblend editorial team. Source paper, Abbas Malik, Saeed, Shehzad and Iqbal, Biomedical Signal Processing and Control, 2025. brain tumor classification EfficientNetV2S Swin Transformer dual attention MRI preprocessing Grad-CAM Four classes, three datasets, one backbone with two additions. DEF-SwinE2NET sorts glioma, meningioma, pituitary tumor and healthy scans. A brain MRI does not announce

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Unveiling the Power of Generative Adversarial Networks (GANs): A Comprehensive Guide

4 Comments / Machine Learning / Adnan Saeed

In today’s rapidly evolving world of artificial intelligence and machine learning, one technology stands out for its innovative approach to data generation and pattern recognition: Generative Adversarial Networks (GANs). This article dives deep into the realm of GANs, explaining their inner workings, applications, and potential to transform industries. Whether you’re a seasoned data scientist, an

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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

1 Comment / Machine Learning / Adnan Saeed

Chronic wounds affect millions of people worldwide, causing pain, disability, and staggering healthcare costs. According to the Wound Healing Society, over 6.5 million patients in the United States alone suffer from chronic wounds, with treatment expenses surpassing $25 billion annually. Despite advancements in medical technology, analyzing these complex wounds remains a significant challenge. Traditional methods

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bacterial keratitis, fungal keratitis, Vision Transformer, self attention fusion, anterior segment images, ophthalmology deep learning, AUROC AUPRC, corneal infection diagnosis, medical image classification, keratitis diagnosis

Revolutionizing Keratitis Diagnosis: How Vision Transformers Are Transforming Eye Care

2 Comments / Machine Learning / Adnan Saeed

Analysis by the aitrendblend editorial team. Source paper, Won, Kim, Jeon, Cha and Lim, Computers in Biology and Medicine, 2025. bacterial keratitis fungal keratitis vision transformer self attention fusion anterior segment imaging ophthalmology AI Same infected eye, three cameras. A Korean research team taught three Vision Transformers to compare notes before deciding bacteria or fungus.

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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 Explained: Teaching SAM2 to Prompt Itself on Ultrasound

2 Comments / Machine Learning, Image Segmentation / Adnan Saeed

AI FOR MEDICAL IMAGING AND HEALTHCARE · 15 MIN READ · Analysis by the aitrendblend editorial team· Sam2Rad Segment Anything Model SAM2 ultrasound prompt learning musculoskeletal imaging zero shot segmentation A bone outline traced automatically on an ultrasound frame. Image styling is illustrative of the pipeline described in the paper. Give the Segment Anything Model

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

Why 3DL-Net’s Dendritic Neurons Only Help When Paired With Its Pyramid Module

2 Comments / Machine Learning, Image Segmentation, Medical AI / Adnan Saeed

Analysis by the aitrendblend editorial team. Medical review pending, see. Ten minute read. Medical Imaging Segmentation Dendritic Learning Breast Ultrasound Ablation Study Segmentation masks from a breast ultrasound and a lung CT scan, the two imaging types 3DL-Net was tested on. A radiologist scrolling through a breast ultrasound exam is not looking for an average.

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Attention Mechanisms Reshaping Medical Image Segmentation

1 Comment / Image Segmentation, Medical AI / Adnan Saeed

Analysis by the aitrendblend editorial team. 9 minute read. Medical Imaging AI Attention Mechanisms Vision Transformers Mamba State Space Models Segmentation A visual reference for how attention weighting highlights regions of interest during automated medical image segmentation. A radiologist scrolling through a stack of MRI slices at two in the morning does not have time

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