TY  - JOUR
AU  - Fatima, Itrat
AU  - Mahum, Rabbia
PY  - 2025
DA  - 2025/08/27
TI  - Lungs Disease Detection Using Deep Learing
JO  - ICCK Journal of Image Analysis and Processing
T2  - ICCK Journal of Image Analysis and Processing
JF  - ICCK Journal of Image Analysis and Processing
VL  - 1
IS  - 3
SP  - 96
EP  - 106
DO  - 10.62762/JIAP.2025.406591
UR  - https://www.icck.org/article/abs/JIAP.2025.406591
KW  - convolutional neural network
KW  - coronavirus disease of 2019
KW  - pneumonia
KW  - tuberculosis
KW  - medical imaging
AB  - Lung diseases such as COVID-19, pneumonia, and tuberculosis remain major public health challenges worldwide, emphasizing the urgent demand for accurate and efficient diagnostic methods. This research explores the use of a Convolutional Neural Network (CNN)-based framework for binary classification of chest X-ray images to detect abnormalities. The methodology incorporates preprocessing techniques such as image resizing, normalization, data augmentation, and grayscale transformation to improve input data quality. CNN architecture comprising convolutional, pooling, fully connected, and dropout layers were trained and evaluated on publicly available datasets. The model attained a test accuracy of 92%; nevertheless, performance metrics revealed a disparity between the two classified categories. Class 0 (Normal) had precision (83%) and recall (90%), resulting in an F1-score of 0.80, whereas Class 1 (Abnormal) demonstrated higher precision (88%) and recall (90%) with an F1-score of 0.88. This highlights the need for further optimization to enhance the detection of normal cases. The findings underscore the potential of CNNs in automating lung disease detection but also reveal areas for improvement in model robustness and class balance.
SN  - 3068-6679
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Fatima2025Lungs,
  author = {Itrat Fatima and Rabbia Mahum},
  title = {Lungs Disease Detection Using Deep Learing},
  journal = {ICCK Journal of Image Analysis and Processing},
  year = {2025},
  volume = {1},
  number = {3},
  pages = {96-106},
  doi = {10.62762/JIAP.2025.406591},
  url = {https://www.icck.org/article/abs/JIAP.2025.406591},
  abstract = {Lung diseases such as COVID-19, pneumonia, and tuberculosis remain major public health challenges worldwide, emphasizing the urgent demand for accurate and efficient diagnostic methods. This research explores the use of a Convolutional Neural Network (CNN)-based framework for binary classification of chest X-ray images to detect abnormalities. The methodology incorporates preprocessing techniques such as image resizing, normalization, data augmentation, and grayscale transformation to improve input data quality. CNN architecture comprising convolutional, pooling, fully connected, and dropout layers were trained and evaluated on publicly available datasets. The model attained a test accuracy of 92\%; nevertheless, performance metrics revealed a disparity between the two classified categories. Class 0 (Normal) had precision (83\%) and recall (90\%), resulting in an F1-score of 0.80, whereas Class 1 (Abnormal) demonstrated higher precision (88\%) and recall (90\%) with an F1-score of 0.88. This highlights the need for further optimization to enhance the detection of normal cases. The findings underscore the potential of CNNs in automating lung disease detection but also reveal areas for improvement in model robustness and class balance.},
  keywords = {convolutional neural network, coronavirus disease of 2019, pneumonia, tuberculosis, medical imaging},
  issn = {3068-6679},
  publisher = {Institute of Central Computation and Knowledge}
}