  Polycystic Ovary Syndrome (PCOS) is an endrocrinological dysfunction
prevalent among women of reproductive age. PCOS is a combination of syndromes
caused by an excess of androgens - a group of sex hormones - in women.
Syndromes including acne, alopecia, hirsutism, hyperandrogenaemia,
oligo-ovulation, etc. are caused by PCOS. It is also a major cause of female
infertility. An estimated 15% of reproductive-aged women are affected by PCOS
globally. The necessity of detecting PCOS early due to the severity of its
deleterious effects cannot be overstated. In this paper, we have developed
PCONet - a Convolutional Neural Network (CNN) - to detect polycistic ovary from
ovarian ultrasound images. We have also fine tuned InceptionV3 - a pretrained
convolutional neural network of 45 layers - by utilizing the transfer learning
method to classify polcystic ovarian ultrasound images. We have compared these
two models on various quantitative performance evaluation parameters and
demonstrated that PCONet is the superior one among these two with an accuracy
of 98.12%, whereas the fine tuned InceptionV3 showcased an accuracy of 96.56%
on test images.
