Urinary bladder cancer detection using YOLO algorithm

Abstract

Ovaj diplomski rad opisuje detekciju karcinoma mokraćnog mjehura primjenom YOLO (You Only Look Once) algoritma. Opisana je problematika računalnog vida i detekcije objekata te uloga konvolucijskih neuronskih mreža pri rješavanju iste. Konvolucijske neuronske mreže opisane su u pogledu strukture, procesa učenja i vrednovanja detekcije objekata. Predstavljena su rješenja detekcije objekata temeljena na konvolucijskim neuronskim mrežama u obliku algoritama s pristupom u dva i u jednom koraku. Iz algoritama s pristupom u jednom koraku izdvojen je i pobliže opisan YOLO algoritam s naglaskom na svoju najnoviju inačicu YOLOv7. YOLOv7 model učen je detekciji tumora na mokraćnom mjehuru na skupovima podataka koji su se sastojali od CT i MRI slikovnih zapisa frontalnog, horizontalnog i sagitalnog presjeka trbušne šupljine. Učenje za pojedini presjek vršeno je odvojeno i rezultiralo je srednjom prosječnom preciznošću od 94.4%, 85.6% i 96.1% za frontalni, horizontalni i sagitalni presjek, redom. Ispitivanjem modela na novim, modelu nepoznatim, slikama utvrđena je uspješna detekcija karcinoma mokraćnog mjehura.This master's thesis describes urinary bladder cancer detection using YOLO (You Only Look Once) algorithm. It describes the problem of computer vision, object detection, and the role of using convolutional neural networks as its solution. Convolutional neural networks are described in the means of their structure, training process, and object detection evaluation. Solutions of object detection based on convolutional neural networks are introduced as one- and two-stage approach algorithms. From one-stage approach algorithms YOLO algorithm has been chosen and described in more detail with emphasis on its newest version YOLOv7. A YOLOv7 model has been trained to detect tumor on the urinary bladder by using datasets that consisted of CT and MRI images of frontal, horizontal and sagittal plane of the abdomen. Learning process for each plane has been performed separately and it has resulted in 94.4%, 85.6%, and 96.1% mean average precision for frontal, horizontal and sagittal plane, respectively. Model testing on new, to the model unknown, images concluded in successful detection of the urinary bladder cancer