A Review of Skin Cancer Detection: Traditional and Deep Learning-Based Techniques

واحدة من أخطر أنواع السرطان هي سرطان الجلد. إن ارتفاع عدد حالات سرطان الجلد ومعدل الوفيات العالي وتكلفة العلاج الطبي العالية تستدعي الكشف المبكر عن أعراضه. يتم اكتشاف سرطان الجلد والتمييز بينه وبين الميلانوما باستخدام معايير الأورام مثل التماثل واللون والحجم والشكل. ونظرًا لأهمية هذه التحديات، قام الباحثون بتطوير مجموعة متنوعة من النهج للكشف المبكر عن سرطان الجلد. تتم مراجعة هذه المقالة بشكل شامل للتقنيات التقليدية وتقنيات التعلم العميق للكشف المبكر عن سرطان الجلد. يتم تقييم أداء هذه التقنيات بناءً على مقاييس مختلفة، وتحليل المجموعات البيانية المستخدمة للتدريب والاختبار. وتم تحديد الدراسات التي تستخدم تقنيات مثل الفحص السريري و تنظير الجلد والأنسجة الطبية، وتم تحليل بنية الشبكات العصبية العميقة المستخدمة للكشف عن سرطان الجلد. تم تقديم مقارنة شاملة للتقنيات الكلاسيكية وتقنيات التعلم العميق للكشف عن سرطان الجلد في هذه المقالة الاستعراضية.One of the most serious types of cancer is skin cancer. The rising number of skin cancer cases, high mortality rate, and high cost of medical treatment necessitate early detection of its symptoms. Skin cancer is detected and differentiated from melanoma using lesion criteria such as symmetry, color, size, and shape. Given the significance of these challenges, researchers have developed a variety of early-detection approaches for skin cancer. This paper comprehensively reviews classical and deep-learning techniques for detecting early skin cancer. The performance of these techniques is evaluated based on various metrics, and the datasets used for training and testing are analyzed. Studies using techniques such as clinical examination, dermoscopy, and histopathology are identified, and the architecture of the deep neural networks used for skin cancer detection is analyzed. A comprehensive comparison of classical and deep-learning techniques for skin cancer detection is provided in this review paper

Multiclassification of license plate based on deep convolution neural networks

In the classification of license plate there are some challenges such that the different sizes of plate numbers, the plates' background, and the number of the dataset of the plates. In this paper, a multiclass classification model established using deep convolutional neural network (CNN) to classify the license plate for three countries (Armenia, Belarus, Hungary) with the dataset of 600 images as 200 images for each class (160 for training and 40 for validation sets). Because of the small numbers of datasets, a preprocessing on the dataset is performed using pixel normalization and image data augmentation techniques (rotation, horizontal flip, zoom range) to increase the number of datasets. After that, we feed the augmented images into the convolution layer model, which consists of four blocks of convolution layer. For calculating and optimizing the efficiency of the classification model, a categorical cross-entropy and Adam optimizer used with a learning rate was 0.0001. The model's performance showed 99.17% and 97.50% of the training and validation sets accuracies sequentially, with total accuracy of classification is 96.66%. The time of training is lasting for 12 minutes. An anaconda python 3.7 and Keras Tensor flow backend are used

Stacked Cross Validation with Deep Features: A Hybrid Method for Skin Cancer Detection

Detection of malignant skin lesions is important for early and accurate diagnosis of skin cancer. In this work, a hybrid method for malignant lesion detection from dermoscopy images is proposed. The method combines the feature extraction process of convolutional neural networks (CNN) with an ensemble learner called stacked cross-validation (CV). The features extracted by three different CNN architectures, namely, ResNet50, Xception, and VGG16 are used for training of four different baseline classifiers, which are support vector machines, k-nearest neighbors, artificial neural networks, and random forests. The stacked outputs of these classifiers are used to train a logistic regression model as a meta-classifier. The performance of the proposed method is compared with the baseline classifiers trained individually as well as AdaBoost classifier, another ensemble learner. Feature extraction with Xception architecture, outperforms all other benchmark models by achieving scores of 0.909, 0.896, 0.886, and 0.917 for accuracy, F1-score, sensitivity, and AUC, respectively

Sistema inteligente basado en redes neuronales para la identificación de cáncer de piel de tipo melanoma en imágenes de lesiones cutáneas

El cáncer de piel es uno de los tipos de cáncer más predominante en el mundo. Existen dos tipos principales de cáncer de piel: melanoma y no melanoma. Siendo el primero, el más agresivo y mortal. Al igual que con otros tipos de cánceres, la detección temprana y precisa de esta enfermedad en una persona, puede hacer que el tratamiento sea más eficaz y por consiguiente mejorar su calidad de vida. En el presente artículo, plantea como objetivo determinar de forma precisa si una imagen lesión cutánea representa un caso de cáncer de piel de tipo Melanoma, para ello se realizará el desarrollo de un sistema inteligente basado en un método de Aprendizaje Profundo usando Redes Neuronales. Los modelos de redes neuronales, fueron entrenados, validados y evaluados con el conjunto de datos de la competición SIIM-ISIC (SocietyforImagingInformatics in Medicine - International Skin ImagingCollaboration) del año 2020. Como resultado, se logró implementar un sistema inteligente ensamblando un módulo de clasificación de imágenes y un módulo de clasificación de metadata, obteniendo una probabilidad de desempeño de 92.85% Precisión, 71.50% Sensibilidad, 94.89% Especificidad

Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review

Skin lesions known as naevi exhibit diverse characteristics such as size, shape, and colouration. The concept of an "Ugly Duckling Naevus" comes into play when monitoring for melanoma, referring to a lesion with distinctive features that sets it apart from other lesions in the vicinity. As lesions within the same individual typically share similarities and follow a predictable pattern, an ugly duckling naevus stands out as unusual and may indicate the presence of a cancerous melanoma. Computer-aided diagnosis (CAD) has become a significant player in the research and development field, as it combines machine learning techniques with a variety of patient analysis methods. Its aim is to increase accuracy and simplify decision-making, all while responding to the shortage of specialized professionals. These automated systems are especially important in skin cancer diagnosis where specialist availability is limited. As a result, their use could lead to life-saving benefits and cost reductions within healthcare. Given the drastic change in survival when comparing early stage to late-stage melanoma, early detection is vital for effective treatment and patient outcomes. Machine learning (ML) and deep learning (DL) techniques have gained popularity in skin cancer classification, effectively addressing challenges, and providing results equivalent to that of specialists. This article extensively covers modern Machine Learning and Deep Learning algorithms for detecting melanoma and suspicious naevi. It begins with general information on skin cancer and different types of naevi, then introduces AI, ML, DL, and CAD. The article then discusses the successful applications of various ML techniques like convolutional neural networks (CNN) for melanoma detection compared to dermatologists' performance. Lastly, it examines ML methods for UD naevus detection and identifying suspicious naevi

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements