Abstract
Chronic respiratory diseases are very prominent now a days, these lung diseases become severe if not treated on time and may lead to lung cancer. National Cancer Registry Programme, India reported that 49.2% of males and 55.2% of females had been diagnosed with lung cancer in the year 2021. Considering lung abnormalities as a serious problem, In the proposed paper, we come up with a deep ensemble architecture as well as the approach for the data creation. An artificial intelligence (AI) based deep ensembled model is developed which identifies the abnormalities like Cardiomegaly, Collapse, Reticulonodular Pattern, Consolidation, Calcification, Bronchitis, Nodule, Osseous Lesions, Support devices and Pleural Effusion etc. The model also localizes the accurate position where the problem occurs. The mAP values obtained from the localization model on supervised and weakly supervised data are 0.323 and 0.376 respectively.
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Ajad, A., Saini, T., Kumar Niranjan, M., Joshi, A., Kumar Swaroop, M.L. (2023). Deep Ensemble Architecture: A Region Mapping for Chest Abnormalities. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1794. Springer, Singapore. https://doi.org/10.1007/978-981-99-1648-1_28
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DOI: https://doi.org/10.1007/978-981-99-1648-1_28
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