A A Study on the Effectiveness of Fine-Tuning VGG16 Versus Classical Machine Learning and Deep Learning Models for Skin Burn Detection
DOI:
https://doi.org/10.56536/jicet.v4i2.125Abstract
Abstract: Accurate skin burn classification is essential for successful medical diagnosis and treatment. This study focuses on the skin burn dataset of 1000 training images and 150 validation images where recognition performance of various classical machine learning algorithms as well as deep learning models has been analyzed. The classical models of deep learning that have been covered in this paper include, namely, Support Vector Machine (SVM), Random Forest (RF), K- Nearest Neighbors (KNN), and Gradient Boosting Machines (GBM). Also , we evaluate the performance of Convolutional Neural Networks (CNNs) and advanced transfer learning techniques using VGG16 for image recognition, classification and segmentation.
The result appear convincing although classical machine learning methods offered valuable insights, their performance was undermined by deep learning approaches. In particular, the basic CNN model demonstrated average accuracy, but it was the fine-tuning of the VGG16 model that achieved the highest validation accuracy of 82.67%. The fine-tuned VGG16 model significantly outperformed classical models which is easy to fit large dataset for image classification. The fine-tuned VGG16 model which had accuracy scores increase from 65.33% to 73.33%, and hybrid models showed different degrees of improvement.
The study also demonstrates how fine-tuned deep learning models are superior to some approaches in machine learning and offers detailed comparisons of various classification methodologies. The foundations of this study are laid down with the aim of increasing the efficacy of the automatic diagnosis of skin burn analysis and improving the efficiency of medical image analysis.