Dermoscopic image classification using CNN with Handcrafted features

Sekhar, Kotra Sankar Raja; Babu, T. R. Ganesh; Prathibha, G.; Kotra, Vijay; Ming, Long Chiau

doi:10.1016/j.jksus.2021.101550

Cited by 20 publications

(7 citation statements)

References 21 publications

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“…There is also a hybrid approach that involves both automatic feature extraction using CNNs and manual feature extraction to obtain vectors for inclusion in the network upstream of its decision-making process [29,30]. This approach is still little used in general and even more so in the field of dermatology, but it allows for providing additional important information to the network in order to improve its classification accuracy.…”

Section: Skin Lesion Classification With Deep Learning Methodsmentioning

confidence: 99%

Anomaly Detection for Skin Lesion Images Using Convolutional Neural Network and Injection of Handcrafted Features: A Method That Bypasses the Preprocessing of Dermoscopic Images

Grignaffini,

Troiano,

Barbuto

et al. 2023

Algorithms

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Skin cancer (SC) is one of the most common cancers in the world and is a leading cause of death in humans. Melanoma (M) is the most aggressive form of skin cancer and has an increasing incidence rate. Early and accurate diagnosis of M is critical to increase patient survival rates; however, its clinical evaluation is limited by the long timelines, variety of interpretations, and difficulty in distinguishing it from nevi (N) because of striking similarities. To overcome these problems and to support dermatologists, several machine-learning (ML) and deep-learning (DL) approaches have been developed. In the proposed work, melanoma detection, understood as an anomaly detection task with respect to the normal condition consisting of nevi, is performed with the help of a convolutional neural network (CNN) along with the handcrafted texture features of the dermoscopic images as additional input in the training phase. The aim is to evaluate whether the preprocessing and segmentation steps of dermoscopic images can be bypassed while maintaining high classification performance. Network training is performed on the ISIC2018 and ISIC2019 datasets, from which only melanomas and nevi are considered. The proposed network is compared with the most widely used pre-trained networks in the field of dermatology and shows better results in terms of classification and computational cost. It is also tested on the ISIC2016 dataset to provide a comparison with the literature: it achieves high performance in terms of accuracy, sensitivity, and specificity.

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Section: Skin Lesion Classification With Deep Learning Methodsmentioning

confidence: 99%

Anomaly Detection for Skin Lesion Images Using Convolutional Neural Network and Injection of Handcrafted Features: A Method That Bypasses the Preprocessing of Dermoscopic Images

Grignaffini,

Troiano,

Barbuto

et al. 2023

Algorithms

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“…In [128], the addition of features in the layers of a CNN is proposed. Specifically, features are extracted from segmented dermoscopic images and used as additional input to the CNN network layer.…”

Section: Deep-learning Methodsmentioning

confidence: 99%

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

et al. 2022

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Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation of skin lesions is necessary to assess the characteristics of the disease; however, it is limited by long timelines and variety in interpretation. As early and accurate diagnosis of SC is crucial to increase patient survival rates, machine-learning (ML) and deep-learning (DL) approaches have been developed to overcome these issues and support dermatologists. We present a systematic literature review of recent research on the use of machine learning to classify skin lesions with the aim of providing a solid starting point for researchers beginning to work in this area. A search was conducted in several electronic databases by applying inclusion/exclusion filters and for this review, only those documents that clearly and completely described the procedures performed and reported the results obtained were selected. Sixty-eight articles were selected, of which the majority use DL approaches, in particular convolutional neural networks (CNN), while a smaller portion rely on ML techniques or hybrid ML/DL approaches for skin cancer detection and classification. Many ML and DL methods show high performance as classifiers of skin lesions. The promising results obtained to date bode well for the not-too-distant inclusion of these techniques in clinical practice.

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“…Since a segmentation mask that removes all background data sometimes causes a rapid decline in the classifier's performance, segmentation is not performed in this study. The tissue around the skin lesion has valuable information that is lost when the tissue is removed in segmentation, which lowers the classification accuracy [84,85].…”

Section: Feature Extractionmentioning

confidence: 99%

SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

Naeem,

Anees,

Khalil

et al. 2024

Mathematics

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The medical sciences are facing a major problem with the auto-detection of disease due to the fast growth in population density. Intelligent systems assist medical professionals in early disease detection and also help to provide consistent treatment that reduces the mortality rate. Skin cancer is considered to be the deadliest and most severe kind of cancer. Medical professionals utilize dermoscopy images to make a manual diagnosis of skin cancer. This method is labor-intensive and time-consuming and demands a considerable level of expertise. Automated detection methods are necessary for the early detection of skin cancer. The occurrence of hair and air bubbles in dermoscopic images affects the diagnosis of skin cancer. This research aims to classify eight different types of skin cancer, namely actinic keratosis (AKs), dermatofibroma (DFa), melanoma (MELa), basal cell carcinoma (BCCa), squamous cell carcinoma (SCCa), melanocytic nevus (MNi), vascular lesion (VASn), and benign keratosis (BKs). In this study, we propose SNC_Net, which integrates features derived from dermoscopic images through deep learning (DL) models and handcrafted (HC) feature extraction methods with the aim of improving the performance of the classifier. A convolutional neural network (CNN) is employed for classification. Dermoscopy images from the publicly accessible ISIC 2019 dataset for skin cancer detection is utilized to train and validate the model. The performance of the proposed model is compared with four baseline models, namely EfficientNetB0 (B1), MobileNetV2 (B2), DenseNet-121 (B3), and ResNet-101 (B4), and six state-of-the-art (SOTA) classifiers. With an accuracy of 97.81%, a precision of 98.31%, a recall of 97.89%, and an F1 score of 98.10%, the proposed model outperformed the SOTA classifiers as well as the four baseline models. Moreover, an Ablation study is also performed on the proposed method to validate its performance. The proposed method therefore assists dermatologists and other medical professionals in early skin cancer detection.

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Dermoscopic image classification using CNN with Handcrafted features

Cited by 20 publications

References 21 publications

Anomaly Detection for Skin Lesion Images Using Convolutional Neural Network and Injection of Handcrafted Features: A Method That Bypasses the Preprocessing of Dermoscopic Images

Anomaly Detection for Skin Lesion Images Using Convolutional Neural Network and Injection of Handcrafted Features: A Method That Bypasses the Preprocessing of Dermoscopic Images

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images

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