Поиск по каталогу |
(строгое соответствие)
|
- Профессиональная
- Научно-популярная
- Художественная
- Публицистика
- Детская
- Искусство
- Хобби, семья, дом
- Спорт
- Путеводители
- Блокноты, тетради, открытки
Classification of Skin Lesion using Image Processing. Machine Learning, Deep Learning, Ensemble Learning and Shallow Neural Network
В наличии
Местонахождение: Алматы | Состояние экземпляра: новый |
Бумажная
версия
версия
Автор: Ginni Arora
ISBN: 9786206162995
Год издания: 1905
Формат книги: 60×90/16 (145×215 мм)
Количество страниц: 104
Издательство: LAP LAMBERT Academic Publishing
Цена: 34935 тг
Положить в корзину
Позиции в рубрикаторе
Отрасли знаний:Код товара: 759876
Способы доставки в город Алматы * комплектация (срок до отгрузки) не более 2 рабочих дней |
Самовывоз из города Алматы (пункты самовывоза партнёра CDEK) |
Курьерская доставка CDEK из города Москва |
Доставка Почтой России из города Москва |
Аннотация: Skin Cancer is the most dangerous type of cancer. The early detection and action can severely reduce the death rate. The advanced technology like machine learning and deep learning makes the possibility of identifying the accurate type of skin disease. The book covers the concept of artificial intelligence, machine learning, deep learning, ensemble learning and shallow neural network in analysis of skin lesion. It also talks about the computer aided diagnostic system in which complete phases from image acquisition, preprocessing, feature extraction, feature reduction and classification through various algorithms is showcased. The database plays a vital role in accurate classification. In this, we have considered the standard dataset for seven types of skin diseases from International Skin Imaging Collaboration(ISIC). The role of DEnSha(Deep, Ensemble and Shallow neural network) is elaborated for early detection and classification of skin disease keeping the performance measures in consideration.
Ключевые слова: Artificial Intelligence, Machine Learning, Deep Learning, skin cancer, computer vison, image processing, Neural Network, Ensemble learning, bag of feature, computer aided diagnostic system