OPTIMIZING ULTRASOUND IMAGE CLASSIFICATION THROUGH TRANSFER LEARNING: FINE-TUNING STRATEGIES AND CLASSIFIER IMPACT ON PRE-TRAINED INNER-LAYERS

• Published in: Informatyka, automatyka, pomiary w gospodarce i ochronie środowiska Vol. 13; no. 4; pp. 27 - 33
• Main Authors: Bal-Ghaoui, Mohamed, El Yousfi Alaoui, My Hachem, Jilbab, Abdelilah, Bourouhou, Abdennaser
• Format: Journal Article
• Language: English
• Published: Lublin University of Technology 20.12.2023
• Subjects: cancer classification; CNN; fine-tuning; SVM; transfer learning; ultrasound images
• ISSN: 2083-0157; 2391-6761
• DOI: 10.35784/iapgos.4464

Transfer Learning (TL) is a popular deep learning technique used in medical image analysis, especially when data is limited. It leverages pre-trained knowledge from State-Of-The-Art (SOTA) models and applies it to specific applications through Fine-Tuning (FT). However, fine-tuning large models can be time-consuming, and determining which layers to use can be challenging. This study explores different fine-tuning strategies for five SOTA models (VGG16, VGG19, ResNet50, ResNet101, and InceptionV3) pre-trained on ImageNet. It also investigates the impact of the classifier by using a linear SVM for classification. The experiments are performed on four open-access ultrasound datasets related to breast cancer, thyroid nodules cancer, and salivary glands cancer. Results are evaluated using a five-fold stratified cross-validation technique, and metrics like accuracy, precision, and recall are computed. The findings show that fine-tuning 15% of the last layers in ResNet50 and InceptionV3 achieves good results. Using SVM for classification further improves overall performance by 6% for the two best-performing models. This research provides insights into fine-tuning strategies and the importance of the classifier in transfer learning for ultrasound image classification.