AIMC Topic: Image Processing, Computer-Assisted

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AI-driven skin cancer detection from smartphone images: A hybrid model using ViT, adaptive thresholding, black-hat transformation, and XGBoost.

PloS one
Skin cancer is a significant global public health issue, with millions of new cases identified each year. Recent breakthroughs in artificial intelligence, especially deep learning, possess considerable potential to enhance the accuracy and efficiency...

Optimization of deep learning models for inference in low resource environments.

Computers in biology and medicine
Artificial Intelligence (AI), and particularly deep learning (DL), has shown great promise to revolutionize healthcare. However, clinical translation is often hindered by demanding hardware requirements. In this study, we assess the effectiveness of ...

CLT-MambaSeg: An integrated model of Convolution, Linear Transformer and Multiscale Mamba for medical image segmentation.

Computers in biology and medicine
Recent advances in deep learning have significantly enhanced the performance of medical image segmentation. However, maintaining a balanced integration of feature localization, global context modeling, and computational efficiency remains a critical ...

KC-UNIT: Multi-kernel conversion using unpaired image-to-image translation with perceptual guidance in chest computed tomography imaging.

Computers in biology and medicine
Computed tomography (CT) images are reconstructed from raw datasets including sinogram using various convolution kernels through back projection. Kernels are typically chosen depending on the anatomical structure being imaged and the specific purpose...

A hyperspectral imaging dataset and Grassmann manifold method for intraoperative pixel-wise classification of metastatic colon cancer in the liver.

Computers in biology and medicine
Hyperspectral imaging (HSI) holds significant potential for transforming the field of computational pathology. However, the number of HSI-based research studies remains limited, and in many cases, the advantages of HSI over traditional RGB imaging ha...

Hybrid deep learning framework based on EfficientViT for classification of gastrointestinal diseases.

Scientific reports
GI diseases are one of the leading causes of morbidity and mortality worldwide, and early and accurate diagnosis is considered to be very important. Traditional methods like endoscopy take time and depend majorly on the judgment of the physician. The...

An automated hybrid deep learning framework for paddy leaf disease identification and classification.

Scientific reports
In India, agriculture remains the primary source of livelihood for many people. Pathogen attacks in crops and plants significantly diminish both the yield and quality of production, leading to financial losses. As a result, identifying diseases in cr...

FedGAN: Federated diabetic retinopathy image generation.

PloS one
Deep learning models for diagnostic applications require large amounts of sensitive patient data, raising privacy concerns under centralized training paradigms. We propose FedGAN, a federated learning framework for synthetic medical image generation ...

Automatic porcine diarrhea viruses classification using pathological images and hybrid semantic neural network.

Computers in biology and medicine
Porcine epidemic diarrhea is a highly contagious intestinal disease in pigs, caused by various strains of porcine epidemic diarrhea virus (PEDV). The infection rate in suckling piglets can reach 100%. Manual analysis of pathological images is the pri...

EmiNet: Moving bacteria detection on optical endomicroscopy images trained on synthetic data.

Computers in biology and medicine
Pneumonia, a respiratory disease often caused by bacterial infection in the distal lung, necessitates prompt and precise diagnosis, particularly in critical care settings. Optical endomicroscopy (OEM) facilitates real-time acquisition of in vivo and ...