Diagnosis of skin cancer using deep learning (original) (raw)

Diagnosis of Skin Cancer using Deep Learnig

Artificial Intelligence and Communication Technologies

Skin diseases consist of a wide range of ailments that affect the skin, including microbial infections, viral, fungal, allergies, epidermis malignancies, and parasitic diseases. In South-Asian countries like India, people don’t care much about skin conditions. In our country, people prefer home remedies to cure skin conditions instead of visiting a dermatologist which can lead to serious skin conditions. Early diagnosis of skin disease is very important as it can reduce the severity of the condition. Melanoma is the deadliest type of skin cancer and the most prominent form of cancer. Melanoma could be diagnosed early, which would reduce overall illness and death. The odds of dying from the ailment are proportional to the extent of the malignancy, which is proportional to the length of time it has been growing. The keys to early detection are patient self-examination of the skin, full-body skin screenings by a dermatologist, and patient engagement. This work aims to categorize skin c...

Classification of Skin Cancer using Deep Learning

The issue of skin cancer surmising can be ordered into three kinds, from the point of view of information portrayal. The methodologies of the first class depicts the skin infections with unadulterated printed data, as far as fundamental signs, verbal gripes, socioeconomics, straight out signals, and the nearness of some tactile side effects. The second kind of approaches rules the entire skin cancer inquire about network, while visual data separated from skin sore pictures is used to speak to skin infections, similar to the variations of surface highlights. The third one incorporates both visual and literary data, for example, tolerant history and patient communication, to portray the given skin ailments. Early melanoma diagnosis seems to improve patient results and can essentially improve patients survival rate, and skin malignant growth identification can be improved through methodologies, for example, screening patients with centred skin side effects utilizing physician-directed full body skin assessments. Right now we have arranged the Benign and Malignant skin disease utilizing convolutional neural network.

Skin Cancer Prediction using Deep Learning

International Journal of Advanced Research in Science, Communication and Technology

There are over 200 different forms of cancer. Out of 200 cases, melanoma is the most lethal form of skin cancer. The melanoma diagnostic process begins with clinical screening followed by dermatoscopy and histopathological examination. If cutaneous melanoma is detected early, the cure rate is high. The first step in diagnosing cutaneous melanoma is a visual examination of the affected areas of the skin. Dermatologists take the dermatoscopic images of the skin lesions by the high-speed camera, which have an accuracy of 65-80% in the melanoma diagnosis without any additional technical support. With further visual examination by cancer treatment specialists and dermatoscopic images, the overall prediction rate of melanoma diagnosis raised to 75-84% accuracy. The project aims to build an automated classification system based on image processing techniques to classify skin cancer using skin lesions images. There is a necessary need for early detection of skin cancer and can prevent furth...

A Review on Melanoma Skin Cancer Detection using Deep Learning

International Journal of Scientific Research in Science, Engineering and Technology, 2021

Skin cancers are the most widely recognized types of human malignancies in reasonable skinned populaces. Albeit malignant melanoma is the type of skin cancer with the most noteworthy mortality, the non-melanoma skin cancers are undeniably normal. The frequency of both melanoma and non-melanoma skin cancers is expanding, with the quantity of cases being analyzed multiplying roughly at regular intervals. In this way, early finding of skin cancer can lessen mortality of patients. In this paper we are exploring different procedures for beginning period melanoma skin cancer detection. For skin lesion detection pathologists look at biopsies to make diagnostic appraisal to a great extent in light of cell life systems and tissue conveyance yet in numerous examples it is emotional and frequently prompts impressive changeability. While PC diagnostic apparatuses empower target judgments by making utilization of quantitative measures. This paper audits the prior period and current advances for machine aided skin cancer detection.

The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer

Healthcare

Machine learning (ML) can enhance a dermatologist’s work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cance...

Skin cancer classification dermatologist-level based on deep learning model

Acta Scientiarum. Technology

Medical image analysis is a significant burden for doctors, therefore, it is used to supplement image processing. Many medical images are assumed to be diagnosed as accurately as healthcare experts when the precision of image detection and recognition in an image processing approach matches that of a human being. Artificial Intelligence (AI) based predictive modelling is an important component of many healthcare solutions. This paper develops and implements a neural network-based method for skin cancer prediction to expose the neural network's strength in this field. This method determines which form of deep learning is best for diagnosing diseases with an accuracy exceeds human ability in terms of speed and accuracy, and determines the optimum number of layers and neurons in each layer for both Convolutional Neural network (CNN) and Deep Neural Network (DNN) to obtain the best possible precision. The results of the proposed method showed impressive results, especially for CNN. ...

Deep learning in Dermatology for skin Diseases Detection

International Journal of Recent Technology and Engineering

Dermatology is a medical field that treats skin health and diseases. People feeling disease symptoms of an affecting the skin must consult a dermatologist if this stipulation does not respond to home remedy. Early detection and treatment can correct most skin disorders. Basal Cell Carcinoma (BCC), Melanoma and Squamous Cell Carcinoma (SCC) are typically appearing type of skin cancers. The purpose of this effort is to provide a system that can be deployed to classify dermatoscopic images to predict skin diseases with early detection and higher accuracy . This work is a concrete effort to accomplish higher degree of accuracy for clinical usage by implementing advances in soft computing and image processing like deep learning and in-depth neural networks in an early stage for 7 class classification for HAM10000 dataset.

Diagnosis of Melanoma Using Deep Learning

Mathematical Problems in Engineering, 2021

When compared to other types of skin cancer, melanoma is the deadliest. However, those who are diagnosed early on have a better prognosis for the purpose of providing a supplementary opinion to experts; various methods of spontaneous melanoma recognition and diagnosis have been investigated by different researchers. Because of the imbalance between classes, building models from existing information has proven difficult. Machine learning algorithms paired with imbalanced basis training approaches are being evaluated for their performance on the melanoma diagnosis challenge in this study. There were 200 dermoscopic photos in which patterns of skin lesions could be extracted using the VGG16, VGG19, Inception, and ResNet convolutional neural network architectures with the ABCD rule. After employing attribute selection with GS and training data balance using Synthetic Minority Oversampling Technique and Edited Nearest Neighbor rule, the random forest classifier had a sensitivity of nearl...

Automatic Classification of Melanoma Skin Cancer with Deep Convolutional Neural Networks

AI

Melanoma skin cancer is one of the most dangerous types of skin cancer, which, if not diagnosed early, may lead to death. Therefore, an accurate diagnosis is needed to detect melanoma. Traditionally, a dermatologist utilizes a microscope to inspect and then provide a report on a biopsy for diagnosis; however, this diagnosis process is not easy and requires experience. Hence, there is a need to facilitate the diagnosis process while still yielding an accurate diagnosis. For this purpose, artificial intelligence techniques can assist the dermatologist in carrying out diagnosis. In this study, we considered the detection of melanoma through deep learning based on cutaneous image processing. For this purpose, we tested several convolutional neural network (CNN) architectures, including DenseNet201, MobileNetV2, ResNet50V2, ResNet152V2, Xception, VGG16, VGG19, and GoogleNet, and evaluated the associated deep learning models on graphical processing units (GPUs). A dataset consisting of 71...

Skin cancer diagnosis using the deep learning advancements: a technical review

Bulletin of Electrical Engineering and Informatics

It is vital in today's technologically advanced society to combat skin cancer using machines rather than human intervention. Any time the look of the skin changes abnormally, there is a danger that the person might be at risk for skin cancer. Dermatology expertise and computer vision methods must be merged to diagnose melanoma more effectively. Because of this, it is necessary to learn about numerous detection methods to help doctors discover skin cancer at an early stage. This research paper provides a comprehensive technical review of the advancements in using deep learning techniques for the diagnosis of skin cancer. Since skin cancer is so prevalent, early identification is essential for better treatment results. Among the medical uses where deep learning, a kind of machine learning, has shown promise is in the identification of skin cancer. This research investigates the most cutting-edge skin cancer diagnostic deep-learning approaches, datasets, and assessment metrics currently in use. This study discusses the benefits and drawbacks of using deep learning for skin cancer detection. Challenges include ethical and privacy considerations about patient data, the incorporation of models into clinical procedures, and problems with dataset bias and generalisation.