Combining ABCD Rule, Texture Features and Transfer Learning in Automatic Diagnosis of Melanoma

Moura, Nayara; Veras, Rodrigo; Aires, Kelson R. T.; Machado, Vinícius Ponte; Silva, Romuere; Araújo, Flávio H. D.; Claro, Maíla de Lima

doi:10.1109/iscc.2018.8538525

Cited by 10 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For experimentation, our method overall results are significantly better than the previously reported works as 90% [15], 92.1% [16], 67% [22], 90.69% [23], 87.25% [24], 86.21% [33], and 89.0% [34] are some studies that are compared with the proposed framework. The proposed study, significantly achieves better accuracy results as shown in Tab.…”

Section: Comparison Of Proposed Studymentioning

confidence: 77%

“…The ABCD rule and texture features are combined to provide an automatic diagnosis of melanoma cancer. In this study, a computational method using dermoscopic images is developed to help dermatologists differentiate between non-melanoma and melanoma skin lesions [16]. A novel approach is introduced to identify melanoma using the ABCD rule based on mobile devices.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

Manzoor¹,

Majeed²,

Siddique³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Skin diseases effectively influence all parts of life. Early and accurate detection of skin cancer is necessary to avoid significant loss. The manual detection of skin diseases by dermatologists leads to misclassification due to the same intensity and color levels. Therefore, an automated system to identify these skin diseases is required. Few studies on skin disease classification using different techniques have been found. However, previous techniques failed to identify multi-class skin disease images due to their similar appearance. In the proposed study, a computer-aided framework for automatic skin disease detection is presented. In the proposed research, we collected and normalized the datasets from two databases (ISIC archive, Mendeley) based on six Basal Cell Carcinoma (BCC), Actinic Keratosis (AK), Seborrheic Keratosis (SK), Nevus (N), Squamous Cell Carcinoma (SCC), and Melanoma (M) common skin diseases. Besides, segmentation is performed using deep Convolutional Neural Networks (CNN). Furthermore, three types of features are extracted from segmented skin lesions: ABCD rule, GLCM, and in-depth features. AlexNet transfer learning is used for deep feature extraction, while a support vector machine (SVM) is used for classification. Experimental results show that SVM outperformed other studies in terms of accuracy, as AK disease achieved 100% accuracy, BCC 92.7%, M 95.1%, N 97.8%, SK 93.1%, SCC 91.4% with a global accuracy of 95.4%.

show abstract

Section: Comparison Of Proposed Studymentioning

confidence: 77%

Section: Related Workmentioning

confidence: 99%

A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

Manzoor¹,

Majeed²,

Siddique³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…By incorporating a feature selection approach and by combining both CNN features and other handcrafted descriptors, the authors improve their results achieving a value of 98% accuracy. Also, an accuracy of 97.5% is obtained in [24][25] by using handcrafted features, 96.5% accuracy by fusing handcrafted and deep-learning-based features in [26], while feature extraction from pre-trained models achieves 93% in [27].…”

Section: 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃/(𝑇𝑃+𝐹𝑃)+𝑇𝑁/(𝑇𝑁+𝐹𝑁)mentioning

confidence: 99%

Automatic detection of melanoma by deep learning models-based feature extraction and fine-tuning strategy

Barburiceanu

Terebes²

2022

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

Skin cancer is one of the most common and lethal cancers in the world. Even though melanoma accounts for a small fraction of all skin cancer types, it is currently the leading cause of skin cancer fatalities. In order to accurately identify the disease in the initial stages, dermoscopy was designed to help specialists and increase the detection rate. Deep learning has been found in recent research to be particularly beneficial in the detection of skin cancer and medical diagnostics. Within this context, we propose adopting two deep-learning algorithms to detect benign and melanoma skin conditions. The first option entails extracting features from CNN pre-trained networks and applying machine-learning classification to make predictions, while the second explores using a fine-tuning methodology combined with data augmentation. The goal of this work is to compare the two methodologies in terms of classification performance and time efficiency. A public database including dermoscopic images of melanoma and normal skin lesions is used for the experimental evaluation. The experimental evaluation reveals that pre-trained models offer superior performance in terms of accuracy and processing time.

show abstract

“…They applied ABCD rule for extracte of features. GLCM was used as texture descriptor with division of features for GLCM [40].…”

Section: Related Literaturementioning

confidence: 99%

Classification of Dermoscopy Images for Early Detection of Skin Cancer – A Review

Mohammed¹,

Mukti²

2019

IJCA

View full text Add to dashboard Cite

Early diagnosis of skin cancer is essential health requirement for the patient and a critical task for the dermatologist. The factual thinking is that the chance of patient's survival is high if diagnosed early. Analysis of the skin images and dermoscopy is a mandatory for medical professionals to take appropriate decision on treatment. A number of methods have been researched to use automated and computerized system for skin diseases image processing. Various dermoscopy image processing techniques have been reviewed to explore the possible solution to skin diseases and to select an appropriate method for early detection7 of skin diseases. This review work will be a pathway to scientist, research scholars and medical practitioners.

show abstract

Combining ABCD Rule, Texture Features and Transfer Learning in Automatic Diagnosis of Melanoma

Cited by 10 publications

References 21 publications

A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

A Lightweight Approach for Skin Lesion Detection Through Optimal Features Fusion

Automatic detection of melanoma by deep learning models-based feature extraction and fine-tuning strategy

Classification of Dermoscopy Images for Early Detection of Skin Cancer – A Review

Contact Info

Product

Resources

About