The coronavirus disease 2019 (COVID-19) pandemic has underscored the need for efficient diagnostic methods owing to the limitations in sensitivity and time constraints associated with molecular tests such as reverse transcription PCR (RT-PCR). This research aims to enhance the efficiency of COVID-19 and other lung diseases such as pneumonia, tuberculosis, bronchitis, emphysema, asthma, and others diagnoses. As an alternative diagnostic, we considered an approach based on enhanced computed tomography (CT) scan images using deep learning (DL). However, we propose a preprocessing segmentation method to enhance the accuracy of DL-based classification that uses the UNet++ architecture, an encoder-decoder approach in DL. In this architecture, the encoder reduces the image resolution to extract informative feature maps while the decoder returns the resolution to the original size. UNet++ is available in four levels: UNet++ L1, L2, L3, and L4, and its performance is compared to that of several other models, including SegNet, FCANet, and DeepLabV3+. Using two different datasets, RSPHC (Indonesia) and Kaggle, testing was conducted to determine the model with the optimum performance. The criteria used to evaluate model performance included the Dice coefficient and IoU metrics, most efficient computational time, and minimal resource requirements (measured by trainable parameters). The UNet++ L4 model achieved a Dice coefficient of 0.994, IoU of 0.989, computational time of 0.925 s, and 9.16 million trainable parameters on the RSPHC dataset. Whereas on the Kaggle dataset it achieved a Dice coefficient of 0.961, IoU of 0.930, computational time of 1.189 s, and 9.16 million trainable parameters. Therefore, the UNet++ L4 model is ideal for accurate segmentation, computational efficiency, and affordable resource requirements. Thus, this research improves lung disease diagnosis through enhanced CT scan images using DL.
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