COMPUTER VISION AND TRANSFER LEARNING APPLICATION WITH CONVOLUTIONAL NEURAL NETWORKS FOR MELANOMA PREDICTION IN DERMATOLOGICAL IMAGES
DOI:
https://doi.org/10.63330/armv1n10-001Keywords:
Computer Vision, Transfer Learning, Convolutional Neural Networks, Melanoma, Assisted DiagnosisAbstract
This work aims to develop and evaluate a deep learning model for melanoma prediction in dermatological images, using Computer Vision and Transfer Learning techniques with Convolutional Neural Networks (CNNs). The research is based on the EfficientNetB7 architecture, pre-trained on ImageNet and adapted for binary classification between "melanoma" and "non-melanoma". The methodology includes image preprocessing, data augmentation, and fine-tuning steps applied to the public dataset “Melanoma – Dr. Scarlat”, available on Kaggle. The implementation was carried out in Python using libraries such as TensorFlow, Keras, and NumPy. The results showed that the model achieved satisfactory performance, with accuracy and sensitivity metrics adequate for assisted diagnosis tasks. It is concluded that the application of Transfer Learning in CNNs can significantly reduce the need for large amounts of labeled data, making this approach a promising tool for clinical support in early melanoma detection, especially in regions with limited access to specialists.
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