Key points are not available for this paper at this time.
In the era of rapid digital transformation and amidst the unprecedented challenges posed by the COVID-19 pandemic, the imperative for accurate and efficient disease diagnosis has never been more pressing. This study delves into the realm of automated detection of two critical respiratory illnesses, COVID-19 and pneumonia, employing a sophisticated approach that integrates the utilization of three distinct models: a custom model developed in-house, Xception, and DenseNet121. Using CNNs, widely recognized for their exceptional capabilities in pattern recognition within medical images, our research endeavors to give a thorough evaluation of each model's performance. Through meticulous experimentation and rigorous comparative analysis, we aim to elucidate not only the strengths and limitations of these models but also their potential for practical deployment in clinical settings. By juxtaposing the outcomes derived from our custom model with those from the established architectures of Exception50 and DenseNet, we seek to offer a nuanced understanding of their respective efficacies in disease detection. Moreover, this study aspires to contribute substantively to the ongoing discourse in medical image analysis, with the overarching goal of facilitating the methodologies to enhance disease detection accuracy and ultimately improve patient care outcomes amidst the challenges posed by respiratory illnesses such as COVID-19 and pneumonia. Key Words: X-ray Detection, Pneumonia Detection, DeepLearning, Image Analysis, Medical Imaging
Shital Pazare (Fri,) studied this question.