AIMC Topic: Deep Learning

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ArsenicNet: An efficient way of arsenic skin disease detection using enriched fusion Xception model.

PloS one
Arsenic contamination of drinking water is a significant health risk. Countries such as Bangladesh's rural areas and regions are in the red alert zone because groundwater is the only primary source of drinking. Early detection of arsenic disease is c...

Deep learning reconstruction of free-breathing, diffusion-weighted imaging of the liver: A comparison with conventional free-breathing acquisition.

PloS one
This study aimed to compare image quality and solid focal liver lesion (FLL) assessments between free-breathing, diffusion-weighted imaging using deep learning reconstruction (FB-DL-DWI) and conventional DWI (FB-C-DWI) in patients undergoing clinical...

XLLC-Net: A lightweight and explainable CNN for accurate lung cancer classification using histopathological images.

PloS one
Lung cancer imaging plays a crucial role in early diagnosis and treatment, where machine learning and deep learning have significantly advanced the accuracy and efficiency of disease classification. This study introduces the Explainable and Lightweig...

TransembleNet: Enhancing vector mosquito species classification through transfer learning-based ensemble model.

PloS one
Mosquitoes, which belong to diverse species, play a significant role in ecological systems and public health. The accurate identification (classification) of mosquito species is essential for a comprehensive understanding of their ecological roles, b...

A deep learning-based algorithm for the detection of personal protective equipment.

PloS one
Personal protective equipment (PPE) is critical for ensuring the safety of construction workers. However, site surveillance images from construction sites often feature multi-size and multi-scale targets, leading to low detection accuracy for PPE in ...

Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM.

PloS one
Artificial Intelligence (AI) is being integrated into increasingly more domains of everyday activities. Whereas AI has countless benefits, its convoluted and sometimes vague internal operations can establish difficulties. Nowadays, AI is significantl...

Deep learning-based classification of speech disorder in stroke and hearing impairment.

PloS one
BACKGROUND AND OBJECTIVE: Speech disorders can arise from various causes, including congenital conditions, neurological damage, diseases, and other disorders. Traditionally, medical professionals have used changes in voice to diagnose the underlying ...

Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm.

PloS one
Cotton is a major cash crop, and increasing its production is extremely important worldwide, especially in agriculture-led economies. The crop is susceptible to various diseases, leading to decreased yields. In recent years, advancements in deep lear...

InBRwSANet: Self-attention based parallel inverted residual bottleneck architecture for human action recognition in smart cities.

PloS one
Human Action Recognition (HAR) has grown significantly because of its many uses, including real-time surveillance and human-computer interaction. Various variations in routine human actions make the recognition process of action more difficult. In th...

Deep learning-enhanced signal detection for communication systems.

PloS one
Traditional communication signal detection heavily relies on manually designed features, making it difficult to fully characterize the essential characteristics of the signal, resulting in limited detection accuracy. Based on this, the study innovati...