The diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients.

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Sulfonated membranes from highly aromatic copoly(ether–amide)s with sulfonation degrees (SDs) between 20 mol% and 55 mol% were prepared from the combination of diamines, 4,4′-(hexafluoroisopropylidene)-bis-( p-phenyleneoxy)dianiline (HFD), and different molar concentrations of 2,4-diaminobenzenesulfonic acid (DABS) that were reacted with equimolar amounts of isophthalic acid (ISO). The ionic membranes show–SO3H groups in the expected concentrations as assessed by Fourier transform infrared and proton nuclear magnetic resonance. Sulfonated copoly(ether–amide) membranes show the first thermal decomposition at 281°C for the sulfonic moieties and a second thermal loss at 439°C for the rigid nonsulfonated backbone which imparts them their high thermal stability. The ion-exchange capacity and water absorption in the sulfonated copoly(ether–amide)s increase steadily with SD. The copoly(ether–amide) with the larger SD, 55 mol% of DABS, HFD- co-ISO/DABS-55, shows the highest proton conductivity (91 mS cm−1) at 75°C. These results situate HFD- co-ISO/DABS-55 as a possible electrolyte membrane for use in polymer electrolyte membrane fuel cell with high thermal and mechanical stability.