Development of an Efficient Deep Learning Model using DenseNet Architecture for Ovarian Cyst Cancer Prediction

Ovarian cysts are fluid-filled sacs that grow on or in the ovaries, often appearing as a component of the menstrual cycle or due to underlying illness. Most cysts are harmless and asymptomatic, but others can become enlarged, producing pain or complications that necessitate treatment. Proper classification of ovarian cysts is crucial for early detection and proper planning of treatment. This paper introduces a novel deep learning-based system for automatic classification of ovarian cysts employing the DenseNet architecture on ultrasound images. The DenseNet is selected for its capacity to recognize complex patterns and fine details in medical imaging using its densely connected layers. A large dataset of ultrasound images consisting of normal as well as cyst-infected ovaries is acquired and pre-processed for quality improvement and standardization. Transfer learning is used to fine-tune the DenseNet model to enable it to distinguish effectively between various ovarian cyst types. The experimental results prove the high accuracy and stability of the model in classifying ovarian cysts, indicating its potential as a useful diagnostic tool in clinical practice. Thorough validation guarantees the system's reliability and generalizability to new data. This work is a contribution to medical imaging through the provision of a strong and effective method for ovarian cyst classification, thereby aiding healthcare providers in making timely and informed decisions regarding patient care.