Medical images classification using deep learning: a survey

Kumar, Rakesh; Kumbharkar, Pooja; Vanam, Sandeep; Sharma, Sanjeev

doi:10.1007/s11042-023-15576-7

Cited by 2 publications

(1 citation statement)

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“…In recent years, the convergence of artificial intelligence (AI) and healthcare has seen significant progress, particularly in the field of medical image processing. Medical imaging is critical in the diagnosis and treatment of a variety of diseases and ailments, giving doctors essential insights into the internal structures and functions of the human body [1]. However, interpreting medical images can be difficult and time-consuming, often requiring the expertise of radiologists and other medical specialists.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Medical Images Using Convolutional Neural Networks

Yogita K. Desai

2024

jes

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Medical image diagnosis using Convolutional Neural Networks (CNNs) has emerged as a viable way to improve the accuracy and efficiency of disease identification and categorization in clinical settings. In this study, they look at how CNNs can be used to diagnose lung nodules from chest X-ray pictures, to provide insights into the technology's performance and future clinical applications. A dataset of 10,000 tagged chest X-ray pictures showing both benign and malignant lung nodules was obtained and preprocessed using standard methods. The dataset was used to construct and train a proprietary CNN architecture, which was then rigorously evaluated on distinct training, validation, and test sets. The CNN model showed good accuracy (94.8%), sensitivity (92.1%), specificity (96.5%), precision, recall, F1 score, and area under the ROC curve (AUC), indicating its robustness and generalization ability. These findings show that CNN-based diagnostic tools may help radiologists and physicians discover and diagnose lung cancer earlier, improving patient outcomes and optimizing healthcare delivery. However, difficulties such as interpretability, data privacy, and regulatory approval must be addressed before CNNs can be fully utilized in medical imaging. This study emphasizes CNNs' transformative significance in diagnostic medicine and the necessity for additional research and development to realize their full potential in clinical practice.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of Medical Images Using Convolutional Neural Networks

Yogita K. Desai

2024

jes

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A novel lightweight CNN for chest X-ray-based lung disease identification on heterogeneous embedded system

Sanida,

Dasygenis

2024

Appl Intell

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The global spread of epidemic lung diseases, including COVID-19, underscores the need for efficient diagnostic methods. Addressing this, we developed and tested a computer-aided, lightweight Convolutional Neural Network (CNN) for rapid and accurate identification of lung diseases from 29,131 aggregated Chest X-ray (CXR) images representing seven disease categories. Employing the five-fold cross-validation method to ensure the robustness of our results, our CNN model, optimized for heterogeneous embedded devices, demonstrated superior diagnostic performance. It achieved a 98.56% accuracy, outperforming established networks like ResNet50, NASNetMobile, Xception, MobileNetV2, DenseNet121, and ViT-B/16 across precision, recall, F1-score, and AUC metrics. Notably, our model requires significantly less computational power and only 55 minutes of average training time per fold, making it highly suitable for resource-constrained environments. This study contributes to developing efficient, lightweight networks in medical image analysis, underscoring their potential to enhance point-of-care diagnostic processes.

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Medical images classification using deep learning: a survey

Cited by 2 publications

References 153 publications

Diagnosis of Medical Images Using Convolutional Neural Networks

Diagnosis of Medical Images Using Convolutional Neural Networks

A novel lightweight CNN for chest X-ray-based lung disease identification on heterogeneous embedded system

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