Region-of-Interest Based Transfer Learning Assisted Framework for Skin Cancer Detection

Abstract

Melanoma is considered the most serious type of skin cancer. All over the world, the mortality rate is much high for melanoma in contrast with other cancer. There are various computer-aided solutions proposed to correctly identify melanoma cancer. However, the difficult visual appearance of the nevus makes it very difficult to design a reliable Computer-Aided Diagnosis (CAD) system for accurate melanoma detection. Existing systems either uses traditional machine learning models and focus on handpicked suitable features or uses deep learning-based methods that use complete images for feature learning. The automatic and most discriminative feature extraction for skin cancer remains an important research problem that can further be used to better deep learning training. Furthermore, the availability of the limited available images also creates a problem for deep learning models. From this line of research, we propose an intelligent Region of Interest (ROI) based system to identify and discriminate melanoma with nevus cancer by using the transfer learning approach. An improved k-mean algorithm is used to extract ROIs from the images. These ROI based approach helps to identify discriminative features as the images containing only melanoma cells are used to train system. We further use a Convolutional Neural Network (CNN) based transfer learning model with data augmentation for ROI images of DermIS and DermQuest datasets. The proposed system gives 97.9% and 97.4% accuracy for DermIS and DermQuest respectively. The proposed ROI based transfer learning approach outperforms existing methods that use complete images for classification.

Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

“…Researchers in [65] proposed automation of discriminative features extraction via ROIs. The system is also capable of addressing an imbalance of classes through augmenting data.…”

3d-Wavelet Transform Based Skin Cancer Classification of VGG-16 Network Model

“…Intelligent ROI-based TL framework using CNN model with data augmentation is designed in (Ashraf et al, 2020). This reliable CAD system utilizes a k-means algorithm to identify accurate melanoma cancer from e-ISSN : 0976-5166 p-ISSN : 2231-3850 other types of skin cancer.…”

“…Less time-consuming CAD technology is required as the medical field expects efficient and reliable techniques to diagnose these life-threatening diseases that cause high mortality globally for patients. The sample digital skin images are acquired from the Medical Repository PH 2 Database as normal and malignant melanoma skinimages in this research.…”

Skin Cancer Detection: A Review Using Deep Learning Techniques

“…Skin cancer is one of the most active types of cancer in the present decade [1]. As the skin is the body's largest organ, the point of considering skin cancer as the most common type of cancer among humans is understandable [2].…”