Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis

• Published in: Artificial intelligence in medicine Vol. 102; p. 101746
• Main Authors: Lorencin, Ivan, Anđelić, Nikola, Španjol, Josip, Car, Zlatan
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2020
• Subjects: Algorithms; Area Under Curve; Artificial Intelligence; Cystoscopy; Databases, Factual; Deep Learning; Humans; Image Interpretation, Computer-Assisted; Image pre-processing; Laplacian edge detector; Multi-layer perceptron; Neural Networks, Computer; Urinary Bladder - diagnostic imaging; Urinary bladder cancer; Urinary Bladder Neoplasms - diagnosis; Urinary Bladder Neoplasms - diagnostic imaging; Laplacian edge detector; Artificial intelligence; Image pre-processing; Multi-layer perceptron; Urinary bladder cancer
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/j.artmed.2019.101746

•Multilayer perceptron with Laplacian can be used for bladder cancer diagnosis.•With larger input image decrease of AUC value can be noticed.•Maximal AUC value is achieved if MLP with intermediate complexity is utilized.•An expert system composed of several multilayer perceptrons can be utilized. In this paper, the urinary bladder cancer diagnostic method which is based on Multi-Layer Perceptron and Laplacian edge detector is presented. The aim of this paper is to investigate the implementation possibility of a simpler method (Multi-Layer Perceptron) alongside commonly used methods, such as Deep Learning Convolutional Neural Networks, for the urinary bladder cancer detection. The dataset used for this research consisted of 1997 images of bladder cancer and 986 images of non-cancer tissue. The results of the conducted research showed that using Multi-Layer Perceptron trained and tested with images pre-processed with Laplacian edge detector are achieving AUC value up to 0.99. When different image sizes are compared it can be seen that the best results are achieved if 50×50 and 100×100 images were used.