The Computational Effect And Hyperparameters Tuning Of Deep Convolutional Layer Depth Of High- Ranking Tuberculosis Detection Models

Abstract

In the past decade, artificial intelligence (AI) solutions have seen widespread application in medical fields, which include automated detection of breast cancer, brain tumors, physiological monitoring and detection of lung diseases such as pneumonia from chest X-Rays (CXRs). Machine learning, a subset of AI, empowers computers to learn autonomously, without direct human programming, by extracting patterns (feature extraction) from data (images). Deep learning, a specialized branch of machine learning, employs multiple convolutional layers to extract complex features from raw input data. This article examines the impact of varying the convolutional layers in deep learning models on their efficiency, focusing on algorithm complexity and parameter counts. We discuss theoretical foundations, and relevant factors affecting efficiency, and analyze algorithm complexity of high-ranking models developed for detecting tuberculosis from chest x-ray (CXR) using convolutional neural networks.  The number of convolutional layers significantly influences model efficiency, affecting both performance and computational complexity. We could conclude practically that optimal layer depth balances model efficiency, accuracy and resource utilization. This assertion was reached by developing a model with fewer convolutional layer depth using the ResNet18 architecture. The parameters count of the ResNet18 model developed was compared with other model developed to detect tuberculosis from chest X-Ray images. The result of this comparison proved that fewer layer depth with the right hyperparameter tuning can produce a better and more efficient deep learning solutions to societal problems. This article also gives insights for future research and practical applications which includes the exploration of adaptive architectures that can dynamically adjust their depth based on the complexity of the task and available resources. Further research can also scientifically probe other methods of reducing the computational overhead of deep neural networks while maintaining priority for high computational performance, model scalability and efficiency.