RETRACTED ARTICLE: Classification of malignant melanoma and benign skin lesion by using back propagation neural network and ABCD rule

Monisha, M.; Suresh, A.; Bapu, B. R. Tapas; Rashmi, M. R.

doi:10.1007/s10586-018-1798-7

Cited by 21 publications

(11 citation statements)

References 23 publications

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“…This guide consists of looking for specific characteristics that allow detecting the asymmetry (A) of a lesion; the type of border (B) if it is irregular, uneven, or blurred; the color variation (C), with reddish, whitish, and bluish being the most dangerous; and the length of the diameter (D) of the lesion [17]. In [18][19][20][21], different methodologies for melanoma detection use the ABCD rule. Unfortunately, this type of system development is hampered by several challenges, such as the lack of data sets with detailed clinical criteria information, or the subtlety of some diagnostic criteria that makes them difficult to detect.…”

Section: Related Jobsmentioning

confidence: 99%

Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier

et al. 2022

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Any cancer type is one of the leading death causes around the world. Skin cancer is a condition where malignant cells are formed in the tissues of the skin, such as melanoma, known as the most aggressive and deadly skin cancer type. The mortality rates of melanoma are associated with its high potential for metastasis in later stages, spreading to other body sites such as the lungs, bones, or the brain. Thus, early detection and diagnosis are closely related to survival rates. Computer Aided Design (CAD) systems carry out a pre-diagnosis of a skin lesion based on clinical criteria or global patterns associated with its structure. A CAD system is essentially composed by three modules: (i) lesion segmentation, (ii) feature extraction, and (iii) classification. In this work, a methodology is proposed for a CAD system development that detects global patterns using texture descriptors based on statistical measurements that allow melanoma detection from dermoscopic images. Image analysis was carried out using spatial domain methods, statistical measurements were used for feature extraction, and a classifier based on cellular automata (ACA) was used for classification. The proposed model was applied to dermoscopic images obtained from the PH2 database, and it was compared with other models using accuracy, sensitivity, and specificity as metrics. With the proposed model, values of 0.978, 0.944, and 0.987 of accuracy, sensitivity and specificity, respectively, were obtained. The results of the evaluated metrics show that the proposed method is more effective than other state-of-the-art methods for melanoma detection in dermoscopic images.

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Section: Related Jobsmentioning

confidence: 99%

Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier

et al. 2022

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“…Rehman et al presented work that consists of CNN for feature extraction, and after that, they used an ANN classifier for the detection of malignant lesions [ 16 ]. Monisha et al presented ABCD dermoscopy for malignant recognition using backpropagation NN to rearrange harmful stage [ 17 ]. Albahar proposed a system using deep CNN applying a novel regularizer technique [ 18 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning

Ghazal

Hussain

Khan

et al. 2022

Computational Intelligence and Neuroscience

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Skin cancer is a major type of cancer with rapidly increasing victims all over the world. It is very much important to detect skin cancer in the early stages. Computer-developed diagnosis systems helped the physicians to diagnose disease, which allows appropriate treatment and increases the survival ratio of patients. In the proposed system, the classification problem of skin disease is tackled. An automated and reliable system for the classification of malignant and benign tumors is developed. In this system, a customized pretrained Deep Convolutional Neural Network (DCNN) is implemented. The pretrained AlexNet model is customized by replacing the last layers according to the proposed system problem. The softmax layer is modified according to binary classification detection. The proposed system model is well trained on malignant and benign tumors skin cancer dataset of 1920 images, where each class contains 960 images. After good training, the proposed system model is validated on 480 images, where the size of images of each class is 240. The proposed system model is analyzed using the following parameters: accuracy, sensitivity, specificity, Positive Predicted Values (PPV), Negative Predicted Value (NPV), False Positive Ratio (FPR), False Negative Ratio (FNR), Likelihood Ratio Positive (LRP), and Likelihood Ratio Negative (LRN). The accuracy achieved through the proposed system model is 87.1%, which is higher than traditional methods of classification.

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“…The steps followed for achieving a distinctive advancement in the malignant melanoma are acquiring the image, pre-processing, highlighting the character of skin features. The extraction of features are done in an featured image processing way by incorporating ready strategy, recognising the symmetry and border, shades and discovering the dimension [47]. This paper of Pedro Rebouças Filho (2018) presented a novel method of classifying melanoma in an automatic fashion with the support of structural co-occurrence matrix (SCM) having main frequency extracted fromdermoscopy imaging.…”

Section: Related Literaturementioning

confidence: 99%

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

Mohammed¹,

Mukti²

2019

IJCA

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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.

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RETRACTED ARTICLE: Classification of malignant melanoma and benign skin lesion by using back propagation neural network and ABCD rule

Cited by 21 publications

References 23 publications

Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier

Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier

Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning

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

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