Kidney Tumour Detection Using Deep Neural Network (International Journal of Innovative Research in Computer Science & Technology, 2022)
Classifying the malignancy of a renal  tumour is one of the most important urological duties  because it plays a key role in determining whether or not  to undergo kidney removal surgery (nephrectomy).  Currently, the radiological diagnostic made us89++ing  computed tomography (CT) scans determines the  likelihood of a tumour being malignant. However, it's  believed that up to 16 percent of nephrectomies may have  been avoided since a postoperative histological study  revealed that a tumour that had been first identified as  malignant was actually benign. Numerous false-positive  diagnoses lead to unnecessary nephrectomies, which  increase the chance of post-procedural problems. In this  article, we offer a computer-aided diagnostic method that  analyses a CT scan to determine the tumour’s malignancy.  The prediction, which is used to identify false-positive  diagnoses, is carried out following radiological diagnosis.  Our solution can complete this challenge with an F1 score  of 0.84. Additionally, we suggest a cutting-edge method  for knowledge transmission in the medical field using  colorization-based pre-processing, which can raise the F1- score by as much as to 1.8.
Keywords: Deep neural; Renal tumour; CT-Scan; Benign; Malignant