Skin Cancer Classification Using Image Processing and Machine Learning

Javaid, Arslan; Sadiq, Muhammad; Akram, Faraz

doi:10.1109/ibcast51254.2021.9393198

Cited by 66 publications

(19 citation statements)

References 11 publications

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“…Although image augmentation helps balance the number of training data for deep learning, the added data still have a high degree of correlation with the original ones [8]. Another technique to balance the data is using Synthetic Minority Oversampling Technique (SMOTE) [21], but it did not perform well for high dimensionality data and overlapping of classes may occur which generating noisy data. We adopt the traditional route of region segmentation, feature extraction and lesion classification as it is easier to understand.…”

Section: Discussionmentioning

confidence: 99%

“…Among all dermoscopic properties of a lesion, color and texture are the most dominant [17,18]. Grey level co-occurrence matrix (GLCM) is a statistical based texture descriptor that is widely used for skin lesion detection [19][20][21]. In GLCM, the occurrences of two neighbouring pixels of specific grey levels and directions are counted and stored in a matrix.…”

Section: Introductionmentioning

confidence: 99%

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Skin lesion classification using multi-resolution empirical mode decomposition and local binary pattern

et al. 2022

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Skin cancer is the most common type of cancer in many parts of the world. As skin cancers start as skin lesions, it is important to identify precancerous skin lesions early. In this paper we propose an image based skin lesion identification to classify seven different classes of skin lesions. First, Multi Resolution Empirical Mode Decomposition (MREMD) is used to decompose each skin lesion image into a few Bidimensional intrinsic mode functions (BIMF). MREMD is a simplified bidimensional empirical mode decomposition (BEMD) that employs downsampling and upsampling (interpolation) in the upper and lower envelope formation to speed up the decomposition process. A few BIMFs are extracted from the image using MREMD. The next step is to locate the lesion or the region of interest (ROI) in the image using active contour. Then Local Binary Pattern (LBP) is applied to the ROI of the image and its first BIMF to extract a total of 512 texture features from the lesion area. In the training phase, texture features of seven different classes of skin lesions are used to train an Artificial Neural Network (ANN) classifier. Altogether, 490 images from HAM10000 dataset are used to train the ANN. Then the accuracy of the approach is evaluated using 315 test images that are different from the training images. The test images are taken from the same dataset and each test image contains one type of lesion from the seven types that are classified. From each test image, 512 texture features are extracted from the lesion area and introduced to the classifier to determine its class. The proposed method achieves an overall classification rate of 98.9%.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Skin lesion classification using multi-resolution empirical mode decomposition and local binary pattern

et al. 2022

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“…These results justified our choice of techniques for the identification and classification of breast mammograms. The proposed method can also be useful for other types of cancers such as skin cancer [ 48 ], lung cancer [ 49 ], and brain tumor [ 50 ] detection.…”

Section: Discussionmentioning

confidence: 99%

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

AlEisa

Touiti

Alhussan

et al. 2022

Computational Intelligence and Neuroscience

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In this paper, a new classification approach of breast cancer based on Fully Convolutional Networks (FCNs) and Beta Wavelet Autoencoder (BWAE) is presented. FCN, as a powerful image segmentation model, is used to extract the relevant information from mammography images. It will identify the relevant zones to model while WAE is used to model the extracted information for these zones. In fact, WAE has proven its superiority to the majority of the features extraction approaches. The fusion of these two techniques have improved the feature extraction phase and this by keeping and modeling only the relevant and useful features for the identification and description of breast masses. The experimental results showed the effectiveness of our proposed method which has given very encouraging results in comparison with the states of the art approaches on the same mammographic image base. A precision rate of 94% for benign and 93% for malignant was achieved with a recall rate of 92% for benign and 95% for malignant. For the normal case, we were able to reach a rate of 100%.

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“…The most common cancer in the United States is skin cancer, which occurs in the tissues of the largest part of the body, i.e., the skin [ 1 ]. The skin blocks heat, sunlight, wounds, and infections [ 2 ]. The skin has three layers: epidermis , dermis, and hypodermis [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Classification for Melanoma Detection Using XceptionNet

Zadeh

2022

Journal of Healthcare Engineering

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Skin cancer is one of the most common types of cancer in the world, accounting for at least 40% of all cancers. Melanoma is considered as the 19th most commonly occurring cancer among the other cancers in the human society, such that about 300,000 new cases were found in 2018. While cancer diagnosis is based on interventional methods such as surgery, radiotherapy, and chemotherapy, studies show that the use of new computer technologies such as image processing mechanisms in processes related to early diagnosis of this cancer can help the physicians heal this cancer. This paper proposes an automatic method for diagnosis of skin cancer from dermoscopy images. The proposed model is based on an improved XceptionNet, which utilized swish activation function and depthwise separable convolutions. This system shows an improvement in the classification accuracy of the network compared to the original Xception and other dome architectures. Simulations of the proposed method are compared with some other related skin cancer diagnosis state-of-the-art solutions, and the results show that the suggested method achieves higher accuracy compared to the other comparative methods.

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Skin Cancer Classification Using Image Processing and Machine Learning

Cited by 66 publications

References 11 publications

Skin lesion classification using multi-resolution empirical mode decomposition and local binary pattern

Skin lesion classification using multi-resolution empirical mode decomposition and local binary pattern

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

Deep Learning-Based Classification for Melanoma Detection Using XceptionNet

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