Skin Cancer Detection and Classification Based on Deep Learning

Said, Raed A.; Raza, Hammad; Muneer, Salman; Amjad, Kamran; Mohammed, Abdul Salam; Akbar, Syed Shehryar; Zonain, Muhammad; Aslam, Muhammad

doi:10.1109/iccr56254.2022.9996077

Cited by 7 publications

(4 citation statements)

References 51 publications

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“…RNN, a type of DL model, is suitable for ID as it can process sequential data. RNNs can analyze network trafc in real-time to identify anomalies and potential threats by using a memory of past inputs created by looping the output back into the network [41,42,46,47,72,73,75,76,[111][112][113][114][115][116]. Te network can use previous inputs, such as past network trafc patterns, to help identify unusual behavior in the current trafc.…”

Section: Black-box and White-box-based Artificial Intelligence Approa...mentioning

confidence: 99%

“…DL techniques [111] have become popular in ID due to their potential to switch complex relationships and extract relevant features from raw data. Te examples you mentioned, HAST-ID and Non-symmetric Deep AutoEncoder (NDAE), demonstrate the capability of DL to extract both spatial and temporal features and learn a low-dimensional illustration of the information.…”

Section: Black-box and White-box-based Artificial Intelligence Approa...mentioning

confidence: 99%

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A Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A Comprehensive Analysis

Muneer,

Farooq,

Athar

et al. 2024

Journal of Engineering

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Intrusion detection (ID) is critical in securing computer networks against various malicious attacks. Recent advancements in machine learning (ML), deep learning (DL), federated learning (FL), and explainable artificial intelligence (XAI) have drawn significant attention as potential approaches for ID. DL-based approaches have shown impressive performance in ID by automatically learning relevant features from data but require significant labelled data and computational resources to train complex models. ML-based approaches require fewer computational resources and labelled data, but their ability to generalize to unseen data is limited. FL is a relatively new approach that enables multiple entities to train a model collectively without exchanging their data, providing privacy and security benefits, making it an attractive option for ID. However, FL-based approaches require more communication resources and additional computation to aggregate models from different entities. XAI is critical for understanding how AI models make decisions, improving interpretability and transparency. While existing literature has explored the strengths and weaknesses of DL, ML, FL, and XAI-based approaches for ID, a significant gap exists in providing a comprehensive analysis of the specific use cases and scenarios where each approach is most suitable. This paper seeks to fill this void by delivering an in-depth review that not only highlights strengths and weaknesses but also offers guidance for selecting the appropriate approach based on the unique ID context and available resources. The selection of an appropriate approach depends on the specific use case, and this work provides insights into which method is best suited for various network sizes, data availability, privacy, and security concerns, thus aiding practitioners in making informed decisions for their ID needs.

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Section: Black-box and White-box-based Artificial Intelligence Approa...mentioning

confidence: 99%

Section: Black-box and White-box-based Artificial Intelligence Approa...mentioning

confidence: 99%

A Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A Comprehensive Analysis

Muneer,

Farooq,

Athar

et al. 2024

Journal of Engineering

Self Cite

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“…In this work, we use the Human Against Machine 10000 (HAM10000) data set, a popular one for skin neoplasm study. HAM10000 is created by Philipp Tschandl, Cliff Rosendahl, and Harald Kittler at the Medical University of Graz in Austria and is available via Kaggle Web site. − HAM10000 data set has more than 10 000 training images for detection of pigmented skin lesions with the following seven classes. Melanoma (MEL) is considered the most serious and potentially life-threatening type of skin cancer, which develops from pigment-producing cells.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Approaches for Skin Neoplasm Diagnosis

Asaduzzaman,

Thompson,

Uddin

2024

ACS Omega

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Approaches for skin neoplasm diagnosis include physical exam, skin biopsy, lab tests of biopsy samples, and image analyses. These approaches often involve error-prone and time-consuming processes. Recent studies show that machine learning shows promise in effectively classifying skin images into different categories such as melanoma and melanocytic nevi. In this work, we investigate machine learning approaches to enhance the performance of computer-aided diagnosis (CADx) systems to diagnose skin diseases. In the proposed CADx system, generative adversarial network (GAN) discriminator is used to identify (and remove) fake images. Exploratory data analysis (EDA) is applied to normalize the original data set for preventing model overfitting. Synthetic minority oversampling technique (SMOTE) is employed to rectify class imbalances in the original data set. To accurately classify skin images, the following machine learning models are utilized: linear discriminant analysis (LDA), support vector machine (SVM), convolutional neural network (CNN), and an ensemble CNN−SVM. Experimental results using the HAM10000 data set demonstrate the ability of the machine learning models to improve CADx performance in treating skin neoplasm. Initially, the LDA, SVM, CNN, and ensemble CNN−SVM show 49%, 72%, 77%, and 79% accuracy, respectively. After applying GAN (discriminator) and SMOTE, the LDA, SVM, CNN, and ensemble CNN−SVM show 76%, 83%, 87%, and 94% accuracy, respectively. We plan to explore other machine learning models and data sets in our next endeavor.

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“…[35][36][37][38] HAM10000 dataset has more than 10,000 training images for detection of pigmented skin lesions with the following seven classes. Melanoma (MEL) is considered the most serious and potentially life-threatening type of skin cancer, which develops from pigment-producing cells.…”

mentioning

confidence: 99%

Machine Learning Approaches for Skin Neoplasm Diagnosis

Asaduzzaman,

Thompson,

Uddin

2024

Preprint

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Approaches for skin neoplasm diagnosis include physical exam, skin biopsy, lab tests of biopsy samples, and image analyses. These approaches often involve error-prone and time-consuming processes. Recent studies show that machine learning has promises to effectively classify skin images into different classes such as melanoma and melanocytic nevi. In this work, we investigate machine learning approaches to enhance the performance of computer-aided diagnosis (CADx) systems to diagnose skin diseases. In the proposed CADx system, generative adversarial network (GAN) is used to identify (and remove) fake images. Exploratory data analysis (EDA) is applied to normalize the original dataset for preventing model overfitting. Synthetic minority over-sampling technique (SMOTE) is employed to rectify class imbalances in the original dataset. To accurately classify skin images, the following four machine learning models are utilized: linear discriminant analysis (LDA), support vector machine (SVM), convolutional neural network (CNN), and an ensemble CNN-SVM. Experimental results using the HAM10000 dataset demonstrate the ability of the machine learning models to improve CADx performance in treating skin neoplasm. Initially, the LDA, SVM, CNN, and ensemble CNN-SVM show 49%, 72%, 77%, and 79% accuracy, respectively. After applying GAN, EDA, and SMOTE, the LDA, SVM, CNN, and ensemble CNN-SVM show 76%, 83%, 87%, and 94% accuracy, respectively. We plan to explore other machine learning models and datasets in our next endeavor.

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Skin Cancer Detection and Classification Based on Deep Learning

Cited by 7 publications

References 51 publications

A Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A Comprehensive Analysis

A Critical Review of Artificial Intelligence Based Approaches in Intrusion Detection: A Comprehensive Analysis

Machine Learning Approaches for Skin Neoplasm Diagnosis

Machine Learning Approaches for Skin Neoplasm Diagnosis

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