Home / Current Issue / Paper 1723529
CliniScan+: An Integrated AI – Based Lung Disease Detection Diagnosis and Smart Healthcare Support System
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence, Medical domain
DOI: 10.64388/IREV10I3-1723529
Abstract
lung diseases are among the major health conditions requiring timely diagnosis and appropriate medical consultation. Chest X-ray imaging is widely used as an initial diagnostic investigation; however, conventional analysis depends heavily on expert interpretation and may be time-consuming. Existing artificial intelligence-based diagnostic systems primarily focus on disease classification and often provide limited integration with healthcare support services. This paper presents CliniScan+, an integrated AI-based healthcare support system for lung disease detection using chest X-ray images. The proposed system employs a Convolutional Neural Network (CNN) for automated multi-disease classification and YOLOv11 for visual localization of relevant regions in chest X-ray images, thereby providing an interpretable representation of the model output. In addition to AI-based image analysis, the system incorporates a doctor recommendation module that assists users in identifying suitable medical specialists based on the predicted condition. A consultation tracking module maintains appointment and follow-up information to support continuity of healthcare services. The system is implemented using a Python-based backend and MongoDB for data management. The integration of disease classification, visual explanation, doctor recommendation, and consultation tracking provides a unified healthcare-support platform rather than an isolated prediction model. Experimental evaluation is performed using classification and detection metrics to assess the performance of the implemented models.
Keywords
Artificial Intelligence, Chest X-ray, Convolutional Neural Network, YOLOv11, Explainable AI, Lung Disease Detection, Healthcare Recommendation System, Consultation Tracking.
References
[1] M. Ennab and H. Mcheick, “Enhancing Pneumonia Diagnosis Through AI Interpretability: Comparative Analysis of Pixel-Level Interpretability and Grad-CAM on X-ray Imaging with VGG19,” 2025. IEEE
[2] N. Shilpa, W. Ayeesha, and P. B. Metre, “Revolutionizing Pneumonia Diagnosis: AI-Driven Deep Learning Framework for Automated Detection from Chest X-Rays,” IEEE Access, 2024. IEEE
[3] E. ^ahin et al., Pneumonia Detection Using Deep Learning and Grad-CAM++ Visualization, 2024.
[4] A. Ayad, Y.-H. Tai, G. Dartmann, and A. Schmeink, A Distributed Medical Recommender System for Patients in the ICU Using Neural Networks, IEEE Access, 2024.
[5] S. M. Mirhoseini Nejad et al., “ConvLSTM–ViT: A Deep Neural Network for Prediction Using Earth Observations and Remotely Sensed Data,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024.
[6] C. Liu et al., “RF-KDE-QSR Model for Estimating the Scale of Epidemics,” IEEE Transactions on Computational Social Systems, 2025. IEEE
[7] B. Behara, M. Delrobaei, and N. Afraz, “Trusted Blockchain-Based Clinical Decision and Medication Management System for Movement Disorders,” IEEE Access, 2025. IEEE
[8] ëÚɵœµ€œhœµµQµœµ5œhœ6�j“¯heÔheÔCJOJQJU\�aJmH @sH @-heÔheÔ6�CJOJQJ\�]�aJmH @sH @.heÔheÔ0J>*CJOJQJ\�aJmH @sH @6�jO¯heÔheÔCJOJQJU\�aJmH @sH @0jheÔheÔCJOJQJU\�aJmH @sH @'heÔheÔCJOJQJ\�aJmH @sH @!heÔCJOJQJ\�aJmH @sH @!hhCJOJQJ\�aJmH @sH @'h¥QQhhCJOJQJ\�aJmH @sH @ [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]
[9] [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]
How to cite this paper
@article{1723529,
author = {Rishika Sharma, Shiva Kumar Prajapathi H, Yudhil Krishna, Vedashree R, Dr. Latha P H},
title = {CliniScan+: An Integrated AI – Based Lung Disease Detection Diagnosis and Smart Healthcare Support System},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {3},
pages = {3587-3597},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1723529.pdf},
abstract = {lung diseases are among the major health conditions requiring timely diagnosis and appropriate medical consultation. Chest X-ray imaging is widely used as an initial diagnostic investigation; however, conventional analysis depends heavily on expert interpretation and may be time-consuming. Existing artificial intelligence-based diagnostic systems primarily focus on disease classification and often provide limited integration with healthcare support services.
This paper presents CliniScan+, an integrated AI-based healthcare support system for lung disease detection using chest X-ray images. The proposed system employs a Convolutional Neural Network (CNN) for automated multi-disease classification and YOLOv11 for visual localization of relevant regions in chest X-ray images, thereby providing an interpretable representation of the model output. In addition to AI-based image analysis, the system incorporates a doctor recommendation module that assists users in identifying suitable medical specialists based on the predicted condition. A consultation tracking module maintains appointment and follow-up information to support continuity of healthcare services. The system is implemented using a Python-based backend and MongoDB for data management.
The integration of disease classification, visual explanation, doctor recommendation, and consultation tracking provides a unified healthcare-support platform rather than an isolated prediction model. Experimental evaluation is performed using classification and detection metrics to assess the performance of the implemented models.},
keywords = {Artificial Intelligence, Chest X-ray, Convolutional Neural Network, YOLOv11, Explainable AI, Lung Disease Detection, Healthcare Recommendation System, Consultation Tracking.},
month = {September},
doi = {https://doi.org/10.64388/IREV10I3-1723529}
}