Journal of Information Technology Management

Journal of Information Technology Management

Implementing a Convolutional Neural Network with Adam Optimizer for Pneumonia Detection Through Chest X-ray Imaging

Document Type : Research Paper

Authors
Sajad Salam Mohammed 1
Aya R. Abih 2
Modhi Lafta Mutar 3
Asaad Shakir Hameed 4
Saja Jumaa Hammad 5
Ahmed K. Jawad Alataby 6
1 Department of Electrical and Electronics Engineering, College of Engineering, University of Thi-Qar, Thi-Qar, Iraq
2 University of Sumer, Thi-Qar, Iraq.
3 Presidency of the University, Shatrah University, Thi-Qar, Iraq.
4 Presidency of the University, Shatrah University, Thi-Qar, Iraq; Mazaya University College, Thi-Qar, Iraq.
5 Immediate Ambulance Department, Al-Dour Technical Institute, Northern Technical University, Tikrit, Iraq
6 General Directorate of Thi-Qar Education, Ministry of Education, Thi-Qar, Iraq.
10.22059/jitm.2026.108393
Abstract
Pneumonia occurs frequently and can be life-threatening all over the world, mainly in locations where radiologists are hard to find. Even though chest X-ray imaging is widely used, correctly and promptly interpreting it is difficult due to human error and inadequate resources. It aims to overcome the problem of limited, reliable resources for advanced radiology by designing and evaluating a CNN model that can distinguish chest X-rays with and without pneumonia. To increase the usability of the model, the chest X-ray images were processed by being resized to a size of 224×224×3 and then enlarged through random rotation, flipping, and translation. The data was split so that 80 percent went to the training set and 20 percent to the testing set. Several layers were built into the custom CNN for overfitting prevention, such as convolution, pooling, batch normalization, ReLU, and dropout. For 20 epochs, I used Adam as the optimizer and set the mini-batch size to 32 and the learning rate to 1e-4. The model correctly distinguished between pneumonia and normal lung cases over 95% of the time. We found that the model was robust thanks to the confusion matrix and a close study of sample images after prediction. Microwave-based deep learning is introduced in this paper, which demonstrates strong accuracy in detecting pneumonia and makes it possible to use the tool as an automatic device for medical and remote healthcare screening.
Keywords
Convolutional Neural Network (CNN)
Chest X-rays
Accuracy Alongside Precision Recall
ReLU
Confusion Matrix Evaluation
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Journal of Information Technology Management
Volume 18, Issue 3
2026
Pages 243-258