https://easychair.org/publications/preprint_open/L9ND

Lightweight deep learning models for detecting COVID-19 from chest X-ray images.

Of all suspicious pigmented skin lesions considered for analysis, a large portion is often benign. The pressure of pathology services and secondary care must be reduced throughout the patient trials using modern techniques for improving the melanoma diagnosis accuracy. Dermoscopic images obtained from digital single-lens reflex (DSLR) cameras, smartphones and a lightweight USB camera are compared using artificial intelligence (AI) algorithm for determining the accuracy of melanoma identification. Datasets are obtained from thousand test samples undergoing plastic surgery. The diagnostic trial is masked, single arm and multicentered. The controlled and suspicious skin lesions as well as the suspicious pigmented skin lesion are captured on the aforementioned cameras while scheduling for biopsy. The possibility of melanoma is assessed using deep learning (DL) techniques on the pigmented skin lesions seen in the dermascopic images for identifying melanoma. For this purpose, we train a deterministic AI algorithm based on malignancy recognition by deep ensemble and inputs from clinicians. The histopathology diagnosis is used as a standard criterion for determining the specialist assessment, algorithmic specificity, sensitivity and the area under the receiver operating characteristic curve (AUROC).

https://irojournals.com/aicn/V3/I1/03.pdf

Artificial Intelligence Algorithm with SVM Classification using Dermascopic Images for Melanoma Diagnosis

Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today’s medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient’s death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today’s healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals.

/pdf/an-efficient-deep-learning-based-skin-cancer-classifier-for-1b8kwgxt.pdf

An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset

Cutaneous melanoma (CM) is a malignant tumor formed from pigment-producing cells called melanocytes. It is one of the most aggressive and fatal forms of skin malignancy. In the last decades, CM's incidence has gradually risen, with 351,880 new cases in 2015. Since the 1960s, its incidence has increased steadily, in 2019, with approximately 96,000 new cases. A greater understanding of early diagnosis and management of CM is urgently needed because of the high mortality rates due to metastatic melanoma. Timely detection of melanoma is crucial for successful treatment, but diagnosis with histopathology may also pose a significant challenge to this objective. Early diagnosis and management are essential and contribute to better survival rates of the patient. To better control this malignancy, such information is expected to be particularly useful in the early detection of possible metastatic lesions and the development of new therapeutic approaches. This article reviews the available information on the early diagnosis and management of CM and discusses such information's potential in facilitating the future prospective.

/pdf/cutaneous-malignant-melanoma-a-review-of-early-diagnosis-and-1vfc07u790.pdf

Cutaneous Malignant Melanoma: A Review of Early Diagnosis and Management

We carry out a critical assessment of machine learning and deep learning models for the classification of skin tumors. Machine learning (ML) algorithms tested in this work include logistic regression, linear discriminant analysis, k-nearest neighbors classifier, decision tree classifier and Gaussian naive Bayes, while deep learning (DL) models employed are either based on a custom Convolutional Neural Network model, or leverage transfer learning via the use of pre-trained models (VGG16, Xception and ResNet50). We find that DL models, with accuracies up to 0.88, all outperform ML models. ML models exhibit accuracies below 0.72, which can be increased to up to 0.75 with ensemble learning. To further assess the performance of DL models, we test them on a larger and more imbalanced dataset. Metrics, such as the F-score and accuracy, indicate that, after fine-tuning, pre-trained models perform extremely well for skin tumor classification. This is most notably the case for VGG16, which exhibits an F-score of 0.88 and an accuracy of 0.88 on the smaller database, and metrics of 0.70 and 0.88, respectively, on the larger database.

/pdf/machine-learning-and-deep-learning-algorithms-for-skin-js7jthw2.pdf

Machine Learning and Deep Learning Algorithms for Skin Cancer Classification from Dermoscopic Images

Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities.

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Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities.