Abstract:
The evaluation of the effectiveness of the automatic computer diagnostic (ACD)
systems developed based on two classifiers – HAAR features cascade and AdaBoost for the
laparoscopic diagnostics of appendicitis and ovarian cysts in women with chronic pelvic pain
is presented. The training of HAAR features cascade, and AdaBoost classifiers were
performed with images/ frames, which have been extracted from video gained in laparoscopic
diagnostics. Both gamma-corrected RGB and RGB converted into HSV frames were used for
training. Descriptors were extracted from images with the method of Local Binary Pattern
(LBP), which includes both data on color characteristics («modified color LBP» - MCLBP)
and textural characteristics, which have been used later on for AdaBoost classifier training.
Classification of test video images revealed that the highest recall for appendicitis diagnostics
was achieved after training of AdaBoost with MCLBP descriptors extracted from RGB
images – 0.708, and in the case of ovarian cysts diagnostics – for MCLBP gained from RGB
images – 0.886. Developed AdaBoost-based ACD system achieved a 73.6% correct classification rate
(accuracy) for appendicitis and 85.4% for ovarian cysts. The accuracy of the HAAR features
classifier was highest in the case of ovarian cysts identification and achieved 0.653 (RGB) –
0.708 (HSV) values. It was concluded that the HAAR feature-based cascade classifier turned
to be less effective when compared with the AdaBoost classifier trained with MCLBP
descriptors. Ovarian cysts were better diagnosed when compared with appendicitis with the
developed ACD.
