AUTHOR=Albalawi Eid , Neal Joshua Eali Stephen , Joys N. M. , Bhatia Khan Surbhi , Shaiba Hadil , Ahmad Sultan , Nazeer Jabeen TITLE=Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation JOURNAL=Frontiers in Medicine VOLUME=Volume 11 - 2024 YEAR=2024 URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1429291 DOI=10.3389/fmed.2024.1429291 ISSN=2296-858X ABSTRACT=Our research addresses the critical need for accurate segmentation in medical healthcare applications, particularly in lung nodule detection using Computed Tomography (CT). Our investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning. We proposed a novel segmentation model to identify lung disease from CT scans to achieve this. We proposed a learning architecture that combines U-Net with a Two-parameter logistic distribution for accurate image segmentation; this hybrid model is called U-Net++, leveraging Contrast Limited Adaptive Histogram Equalization (CLAHE)on a 5000 set of CT scan images. Our model was trained and evaluated using several deep learning classifiers on the LUNA-16 dataset, achieving superior performance in terms of the Probabilistic Rand Index (PRI), Variation of Information (VOI), Region of Interest (ROI), Dice Coefficient, and Global Consistency Error (GCE). The evaluation demonstrated a high accuracy of 91.76% for parameter estimation, confirming the effectiveness of the proposed approach.