Kidney tumor detection and classification using image processing

Abstract

Kidney cancer is a disorder where the cells of the kidney become abnormal and develop into a tumor. It is also referred to as renal cell carcinoma. The detection of kidney tumor in the early stages is very important; it is said that different imaging techniques can help doctors to decide the cancer stage and determine the appropriate treatment method. The detection and diagnosis of kidney tumors are performed by scanning images of computed tomography. Most existing works in renal kidney cancer is detection and diagnosis of the existence or absence of tumor in the kidney and classify the tumor as harmless or malevolent. The fundamental goal of this paper is to propose a strategy that will characterize the type of cells present in the kidney tumor. This can be accomplished through methods like segmentation, feature extraction using GLCM and classi ication using multiSVM.

A Comprehensive Review of Artificial Intelligence Approaches in Kidney Cancer Medical Images Diagnosis, Datasets, Challenges and Issues and Future Directions

“…Methods/Techniques Brief Description Dataset Strengths Weaknesses Rohith V et.al (2019) [2] Multi SVM classification model…”

“…CT scans are an invaluable tool for analyzing the kidneys to identify disorders that include tumors, ulcers, kidney stones, congenital defects, polycystic nephropathy, a buildup of liquid around the kidneys, and abscesses. [2] . TCIA supported by the National Cancer Institute, Bethesda, Maryland, USA [17] .…”

“…Surgery, targeted treatments, immunotherapy, and radiation therapy are all part of the treatment options available for cancer, which might differ according to the patient's condition, the stage of the disease, and the particular form of cancer. The prognosis and results for patients identified as having kidney cancer are greatly affected by the lack of early identification, which is why periodic medical examinations are necessary [1] [2] . By providing advanced diagnostic methods and furnishing cutting-edge analytical tools, radiomics and artificial intelligence (AI) are reshaping the assessment of kidney lesions.…”

“…Magnetic resonance imaging (MRI), Computed tomography (CT), and ultrasound are often employed imaging techniques in traditional kidney cancer diagnoses. However, radiologists need experience IJMSCS ISSN: 2704-1077 eISSN 2704-1069 Journal homepage: https://ijmscs.org/ Many studies made on the kidney diagnosis such as [2]. The primary focus of the proposed strategy is on the segments and classifications.…”