Design and Development of Deep Learning Models for Biomedical Image Analysis in Advancing Respiratory Disease Diagnosis
Keywords:
Respiratory Disease Diagnosis, Biomedical Image Analysis, Deep Learning Models, Convolutional Neural Networks (CNNs), Transfer LearningAbstract
As a major world health problem, respiratory illnesses need new ways to be diagnosed so they can be found early and correctly. This study is mostly about creating and improving advanced deep learning models for biological picture analysis to make it easier to diagnose lung illnesses. Using the strength of convolutional neural networks (CNNs) and residual neural networks (RNNs), our suggested models are meant to pull out complex patterns and time-dependent relationships from a range of biological imaging types, including X-rays, CT scans, and microscopic pictures. As a first step in our study, we use preparation and addition methods to make the models more stable by dealing with inconsistent and limited data. After that, a CNN-RNN design that combines the best features of both networks is suggested as a way to record both the spatial and temporal changes of biological pictures. People use transfer learning techniques to use models that have already been trained on big datasets, which improves performance even when there isn't a lot of named biological data. So that the models are useful in real life, they are trained and tested on large datasets that come from a wide range of groups and include a wide range of lung diseases. The evaluation criteria include sensitivity, specificity, and total accuracy, which give a full picture of how well the models can diagnose. Also, explainability methods are used to make the decision-making process more open and clear. This makes healthcare workers more likely to believe the suggested deep learning models. The results of this study could greatly improve the way lung diseases are diagnosed, allowing for early care and customized plans. Deep learning is always getting better, and our models help with the ongoing work to use AI to make healthcare better, especially for people with lung illnesses.
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