Precision Kidney Disease Classification Using EfficientNet-B3 and CT Imaging

Kidney disorders are a prominent issue in global health, and the quick and precise identification of these conditions is crucial for the optimal care of patients. This study introduces an innovative methodology for the categorization of kidney diseases by using advanced deep learning algorithms. In this study, we conducted fine-tuning of the EfficientNet-B3 architecture using a large dataset of CT kidney images. The dataset consisted of four unique classes, namely Normal, Cyst, Tumour, and Stone. The dataset exhibits a wide range of variations, enabling a thorough evaluation of kidney disorders often seen in the context of clinical practice. Through the use of this dataset for model training, a notable classification accuracy of 95.77% was attained, hence showcasing the efficacy of our methodology in discerning between distinct kidney diseases. The considerable practical value of the model's high accuracy lies in its capacity to accelerate the diagnostic process and provide valuable assistance to healthcare professionals in making well-informed judgements. The present study work offers a comprehensive description of the methods used, including data preparation, model design, and the fine-tuning procedure. Moreover, it explores the significance of interpretability and model explain ability within the medical field. In addition, we provide a comparative study of our fine-tuned EfficientNet-B3 model in comparison to other frequently used designs, emphasizing its better performance. Moreover, this research highlights the significant importance of deep learning models in enhancing the diagnosis of kidney illness and their capacity to assist healthcare professionals in making more precise and prompt judgements, eventually resulting in improved patient outcomes. The obtained accuracy of 95.77% underscores the potential benefits of using deep learning models in clinical environments for the purpose of classifying and diagnosing renal diseases.