Identification of lung nodules CT scan using YOLOv5 based on convolution neural network

TL;DR

This paper presents a deep learning method using YOLOv5 CNN architecture to accurately detect lung nodules in CT scans, achieving high precision and demonstrating potential for improved diagnostic support.

Contribution

The study introduces a YOLOv5-based approach for lung nodule detection in CT scans, with a focus on high accuracy and practical application in medical diagnostics.

Findings

Achieved 92.27% mean average precision (mAP) in nodule detection.

Demonstrated effective localization of lung nodules in CT images.

Validated the method on a public dataset with promising results.

Abstract

Purpose: The lung nodules localization in CT scan images is the most difficult task due to the complexity of the arbitrariness of shape, size, and texture of lung nodules. This is a challenge to be faced when coming to developing different solutions to improve detection systems. the deep learning approach showed promising results by using convolutional neural network (CNN), especially for image recognition and it's one of the most used algorithm in computer vision. Approach: we use (CNN) building blocks based on YOLOv5 (you only look once) to learn the features representations for nodule detection labels, in this paper, we introduce a method for detecting lung cancer localization. Chest X-rays and low-dose computed tomography are also possible screening methods, When it comes to recognizing nodules in radiography, computer-aided diagnostic (CAD) system based on (CNN) have demonstrated their worth. One-stage detector YOLOv5 trained on 280 annotated CT SCAN from a public dataset LIDC-IDRI based on segmented pulmonary nodules. Results: we analyze the predictions performance of the lung nodule locations, and demarcates the relevant CT scan regions. In lung nodule localization the accuracy is measured as mean average precision (mAP). the mAP takes into account how well the bounding boxes are fitting the labels as well as how accurate the predicted classes for those bounding boxes, the accuracy we got 92.27%. Conclusion: this study was to identify the nodule that were developing in the lungs of the participants. It was difficult to find information on lung nodules in medical literature.