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Polycystic Ovary Syndrome (PCOS) is an endrocrinological dysfunctionprevalent among women of reproductive age. PCOS is a combination of syndromescaused 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 femaleinfertility. An estimated 15% of reproductive-aged women are affected by PCOSglobally. The necessity of detecting PCOS early due to the severity of itsdeleterious effects cannot be overstated. In this paper, we have developedPCONet - a Convolutional Neural Network (CNN) - to detect polycistic ovary fromovarian ultrasound images. We have also fine tuned InceptionV3 - a pretrainedconvolutional neural network of 45 layers - by utilizing the transfer learningmethod to classify polcystic ovarian ultrasound images. We have compared thesetwo models on various quantitative performance evaluation parameters anddemonstrated that PCONet is the superior one among these two with an accuracyof 98.12%, whereas the fine tuned InceptionV3 showcased an accuracy of 96.56%on test images.

PCONet: A Convolutional Neural Network Architecture to Detect Polycystic Ovary Syndrome (PCOS) from Ovarian Ultrasound Images