Location
Harare, Zimbabwe and Virtual
Start Date
13-9-2024 11:25 AM
End Date
13-9-2024 11:50 AM
Description
Accurate and efficient classification of melanoma stages is crucial for effective treatment planning and improved patient outcomes. Traditional diagnostic methods are often time-consuming and subjective, highlighting the need for advanced computational approaches. This study proposes a self-supervised learning framework combined with a convolutional neural network (CNN) to classify melanoma stages more effectively. Initially, features are extracted from unlabeled skin images using pre-trained VGG16 and ResNet50 models. These features are combined and reduced in dimensionality using Principal Component Analysis (PCA). Subsequently, K-means and DBSCAN clustering is applied to pseudo-label the data, providing a foundation for pre-training a CNN model. This pre-trained model is then fine-tuned on a smaller, labelled dataset to enhance classification accuracy. The methodology shows promising results, validated by significant metrics like Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. Experimental results confirm the superior efficacy of this approach, enhancing melanoma diagnosis and treatment planning through advanced deep learning techniques.
Included in
Deep Learning Approach in Melanoma Stage Classification
Harare, Zimbabwe and Virtual
Accurate and efficient classification of melanoma stages is crucial for effective treatment planning and improved patient outcomes. Traditional diagnostic methods are often time-consuming and subjective, highlighting the need for advanced computational approaches. This study proposes a self-supervised learning framework combined with a convolutional neural network (CNN) to classify melanoma stages more effectively. Initially, features are extracted from unlabeled skin images using pre-trained VGG16 and ResNet50 models. These features are combined and reduced in dimensionality using Principal Component Analysis (PCA). Subsequently, K-means and DBSCAN clustering is applied to pseudo-label the data, providing a foundation for pre-training a CNN model. This pre-trained model is then fine-tuned on a smaller, labelled dataset to enhance classification accuracy. The methodology shows promising results, validated by significant metrics like Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. Experimental results confirm the superior efficacy of this approach, enhancing melanoma diagnosis and treatment planning through advanced deep learning techniques.
