Machine Learning Models for the Classification of Skin Cancer

Arooj, Sahar; Khan, Muhammad Farhan; Khan, Muhammad Adnan; Khan, Muhammad Saleem; Taleb, Nasser

doi:10.1109/icbats54253.2022.9759054

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…This CNN ( 29 ) network was modified to our needs, and the pre-processed images were then loaded into the proposed AlexNet transfer learning model ( 30 ). According to the problem, the first and last three layers of the architecture are modified and newly modified layers are the image input layer, fully connected layer, softmax layer, and classification layer, although the remaining layers remain fixed.…”

Section: Proposed Modelmentioning

confidence: 99%

Breast Cancer Detection and Classification Empowered With Transfer Learning

Arooj

Atta-ur-Rahman

Zubair

et al. 2022

Front. Public Health

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Cancer is a major public health issue in the modern world. Breast cancer is a type of cancer that starts in the breast and spreads to other parts of the body. One of the most common types of cancer that kill women is breast cancer. When cells become uncontrollably large, cancer develops. There are various types of breast cancer. The proposed model discussed benign and malignant breast cancer. In computer-aided diagnosis systems, the identification and classification of breast cancer using histopathology and ultrasound images are critical steps. Investigators have demonstrated the ability to automate the initial level identification and classification of the tumor throughout the last few decades. Breast cancer can be detected early, allowing patients to obtain proper therapy and thereby increase their chances of survival. Deep learning (DL), machine learning (ML), and transfer learning (TL) techniques are used to solve many medical issues. There are several scientific studies in the previous literature on the categorization and identification of cancer tumors using various types of models but with some limitations. However, research is hampered by the lack of a dataset. The proposed methodology is created to help with the automatic identification and diagnosis of breast cancer. Our main contribution is that the proposed model used the transfer learning technique on three datasets, A, B, C, and A2, A2 is the dataset A with two classes. In this study, ultrasound images and histopathology images are used. The model used in this work is a customized CNN-AlexNet, which was trained according to the requirements of the datasets. This is also one of the contributions of this work. The results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than the existing models on datasets A, B, C, and A2.

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Section: Proposed Modelmentioning

confidence: 99%

Breast Cancer Detection and Classification Empowered With Transfer Learning

Arooj

Atta-ur-Rahman

Zubair

et al. 2022

Front. Public Health

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“…Skin cancer arises from the unregulated growth of skin cells resulting in the development of malignancies [1]. Ultraviolet radiation exposure without protection is considered the major cause of these malignancies [2][3][4][5][6][7][8][9][10][11]. Melanomas comprise only 5% of all cases of malignant skin growths, whereas basal cell carcinoma and squamous cell carcinoma account for the remaining 95%.…”

Section: Introductionmentioning

confidence: 99%

“…During the last few decades, there has been a steady increase in the incidence of skin cancer [12]. Every year, around 18,000 people are found to have invasive malignant melanoma for the first time [6]. In the United States, skin cancers account for 4% of all malignant neoplasms and roughly 1% of all cancer deaths are attributable to this form of the disease [13].…”

Section: Introductionmentioning

confidence: 99%

DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images

Naeem,

Anees

2024

PLoS ONE

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Skin cancer is a common cancer affecting millions of people annually. Skin cells inside the body that grow in unusual patterns are a sign of this invasive disease. The cells then spread to other organs and tissues through the lymph nodes and destroy them. Lifestyle changes and increased solar exposure contribute to the rise in the incidence of skin cancer. Early identification and staging are essential due to the high mortality rate associated with skin cancer. In this study, we presented a deep learning-based method named DVFNet for the detection of skin cancer from dermoscopy images. To detect skin cancer images are pre-processed using anisotropic diffusion methods to remove artifacts and noise which enhances the quality of images. A combination of the VGG19 architecture and the Histogram of Oriented Gradients (HOG) is used in this research for discriminative feature extraction. SMOTE Tomek is used to resolve the problem of imbalanced images in the multiple classes of the publicly available ISIC 2019 dataset. This study utilizes segmentation to pinpoint areas of significantly damaged skin cells. A feature vector map is created by combining the features of HOG and VGG19. Multiclassification is accomplished by CNN using feature vector maps. DVFNet achieves an accuracy of 98.32% on the ISIC 2019 dataset. Analysis of variance (ANOVA) statistical test is used to validate the model’s accuracy. Healthcare experts utilize the DVFNet model to detect skin cancer at an early clinical stage.

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