Published Paper Details:

The aim of this paper is to automate the Chronic Kidney Disease(CKD) detection process using Lab Test and Computed Tomography(CT) scan images by employing a Deep Learning model using small sample size.Because basic diagnosis is not a straight forward process and needs algorithm based detections. In this paper, we use Bidirectional Long Short-Term Memory and Deep Convolution Neural Network algorithms based on Artificial intelligence(AI) to extract and evaluate the features of different classes using pre-processed CKD datasets to evaluate the CKD early stages. The CT images are classified into four classes Normal, Tumor, Cyst and Stone. In this paper we also find out the precision loss and model loss. The model outperforms traditional data method classification technique by providing much better predictive ability. This small initiative will detect the CKD in early stages by using Deep Learning methods effectively, as it is a work related to medical field for helping the society and saving the human life.