eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-09-30 18 3 2258 2269 10.13005/bpj/3252 67275 article Shital Pawar 1 Pratik Dhane, Dhanraj Shelke 1 Prashant Dheple 1 Nisarg Doshi Department of Electronics and Telecommunication Engineering, Vishwakarma Institute of Technology Pune, India Polycystic ovarian syndrome (PCOS) affects 8-13% of women of reproductive age, with up to 70% of cases being untreated globally. In the proposed methodology, medical images of ovarian ultrasounds are processed through VGGNet-19, extracting high-level features from the images. Simultaneously, clinical text reports, containing valuable diagnostic information, are subjected to a zero-shot learning text classification model. This hybrid architecture enables the utilization of both visual and textual data sources for enhanced PCOS detection. The fusion model leverages VGGNet-19's prowess in image feature extraction, capturing intricate patterns and details within ultrasound images. A dataset of size 2004 images is used for this experiment. XGBoost, known for its robust classification capabilities, processes the extracted features to classify PCOS cases effectively. The proposed system uses both textual and image data for early PCOS detection. The textual data has been made using various factors, including dietary habits, daily routines, and more. The proposed system has the capability for early detection of PCOS while simultaneously identifying concurrent health conditions associated with PCOS like diabetes hypertension etc. The dataset encompasses various parameters, including patient-specific information such as age, weight, height, and BMI, as well as medical measurements like Hb, cycle characteristics, hormone levels, and more. The deep fusion model achieved an accuracy of 99.6% . https://biomedpharmajournal.org/vol18no3/pcos-detection-using-hybrid-cnn-xgboost-model-a-multimodal-data-approach/ CNN Disease Detection Polycystic Ovary Syndrome (PCOS) XGBoost Zero-Shot Learning