LUNG DISEASES FOR PNEUMONIA AND COVID-19 USING MACHINE AND DEEP LEARNING TECHNIQUES

Electrical Engineering

Lung Disease is common throughout the world. These include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, fibrosis, etc. Timely diagnosis of lung disease is essential. Many image processing and machine learning models have been developed for this purpose. The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. Machine learning techniques, ranging from traditional algorithms to sophisticated deep learning models, have proven valuable in the analysis of medical images for lung disease detection. The emergence of CNNs, designed to excel in image analysis tasks, has particularly transformed this field, enabling automated detection and classification with unprecedented accuracy. These technologies not only offer the ability to expedite diagnosis but also to reduce the workload on healthcare professionals. The benefits of utilizing machines and deep learning techniques extend beyond accuracy and speed; they hold the potential to create scalable and robust solutions applicable in diverse healthcare settings, including resource-limited environments. This abstract provides a foundation for a comprehensive exploration of these methods, encompassing their methodologies, challenges, and prospects, as they play a pivotal role in the fight against respiratory diseases, ultimately contributing to more efficient and effective healthcare systems

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