Abstract:
Nasal polyps are benign growths that occur within the sinuses or nasal passages.
Because of their interconnected characteristics and diverse appearance, they present
significant challenges in terms of diagnosis and treatment. For these polyps to be
effectively managed clinically, they must be accurately classified. However, manual
examination-based traditional approaches are complicated and prone to error. In order
to create an automated system that accurately classifies nasal polyps and can
differentiate between antrochoanal polyps (AP) and ethmoidal polyps (EP), this work
makes use of progressive deep learning techniques. Several advanced convolutional
neural networks (CNNs) are used in the study, including ResNet-50, VGG16,
PolyScanCNN (a customised CNN model), and EfficientNetB0. A large dataset of
nasal MRI images, which underwent intensive preprocessing to improve picture
quality and augment the data, served as the training set for these models. The models
were assessed and compared using performance indicators such F1-score, recall,
accuracy, and precision. The ResNet-50 model achieved a 96% accuracy rate,
demonstrating its effectiveness in identifying nasal polyps. The study shows how well
these models work in identifying nasal polyps, with ResNet-50 outperforming the
other models in the majority of evaluation measures. Gradio was used to create an
intuitive web-based interface that allows real-time nasal polyp picture categorization
and visualization, hence promoting clinical adoption. This research highlights how
deep learning can improve otolaryngology diagnostic efficiency and accuracy. It seeks
to assist physicians in making wise decisions by offering a trustworthy tool for the
classification of nasal polyps, ultimately leading to better patient outcomes. The
results indicate that the field of medical image analysis can be greatly advanced by
implementing advanced AI-driven solutions.
