Kidney Tumour Detection Using Deep Neural Network

Tawseeful Haziq; Ashish Obroi; Yogesh

doi:10.55524/ijircst.2022.10.5.2

Abstract

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

References

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Cites this article as

T. Haziq, A. Obroi, Yogesh, "Kidney Tumour Detection Using Deep Neural Network", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-10, Issue-5, Page No-5-12, 2022. Available from: https://doi.org/10.55524/ijircst.2022.10.5.2

Corresponding Author

Tawseeful Haziq

M. Tech Scholar, Department of Computer Science & Engineering, RIMT University, Mandi Gobindgarh, Punjab, India

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