Izvorni znanstveni članak
https://doi.org/10.32985/ijeces.15.4.7
Gray Level Co-occurrence Matrix based Fully Convolutional Neural Network Model for Pneumonia Detection
Shubhra Prakash
orcid.org/0009-0008-5668-5658
; Computer Science Department, CHRIST (Deemed to be University) Bengaluru, India
Bhojan Ramamurthy
; Computer Science Department, CHRIST (Deemed to be University) Bengaluru, India
Sažetak
This study presents a new method to improve the detection ability of a convolutional neural network (CNN) in pneumonia detection using chest X-ray images. Using Gray-Level Co-occurrence Matrix (GLCM) analysis, additional channels are added to the original image data provided by Guangzhou Children's Hospital in Guangzhou, China. The main goal is to design a lightweight, fully convolution network and increase its available information using GLCM. Performance analysis is performed on the new CNN model and GLCM-enhanced CNN model, and results are compared with Transfer Learning approaches. Various evaluation metrics, including accuracy, precision, recall, F1 score, and AUC-ROC, are used to evaluate the improved analysis performance of CNN. The results showed a significant increase in the ability of the model to detect pneumonia, with an accuracy of 99.57%. In addition, the study evaluates the descriptive properties of the CNN model by analyzing its decision process using Grad-CAM.
Ključne riječi
CNN; Pneumonia; Chest X-ray; GLCM; Explainability; Diagnostic;
Hrčak ID:
315582
URI
Datum izdavanja:
28.3.2024.
Posjeta: 92 *