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Comparison of Breast Cancer and Skin Cancer Diagnoses Using Deep Learning Method

机译:利用深层学习方法比较乳腺癌和皮肤癌诊断

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Artificial intelligence applications are of great importance in the solution of cancer, which is one of the biggest health problems of our age. In this study, a study was conducted on deep learning methods that make life important in the early diagnosis of breast cancer and skin cancer, which are among the most common types of cancer worldwide. Breast cancer and skin cancer data were classified as benign and malignant by deep learning methods. While working with the deep learning method, the classification was made using the Convolutional Neural Network (CNN) algorithm. In this classification, the data are divided into benign cancer sets and malignant cancer sets. Finally, the data provided by the logistic regression method were analyzed and success charts were created and both types were compared. As a result, accuracy and loss graphs of both cancer types were formed. The aim of the study is to compare breast cancer and skin cancer with the deep learning method. And some breast cancer and skin cancer diagnoses are confused. In further studies, the basis of differentiating the diagnosis of these two types of cancer from each other was made in this study.
机译:人工智能应用在癌症解决方案中具有重要意义,这是我们这个时代最大的健康问题之一。在这项研究中,对深度学习方法进行了研究,使生活在乳腺癌和皮肤癌的早期诊断中具有重要意义,这些方法是全世界最常见的癌症类型。乳腺癌和皮肤癌数据通过深入学习方法被归类为良性和恶性。在使用深度学习方法的同时,使用卷积神经网络(CNN)算法进行分类。在这种分类中,数据分为良性癌症套和恶性癌症组。最后,分析了逻辑回归方法提供的数据,并创建了成功图表,并进行了两种类型。结果,形成了两种癌症类型的准确性和损耗图。该研究的目的是通过深入学习方法比较乳腺癌和皮肤癌。一些乳腺癌和皮肤癌症诊断都很困惑。在进一步的研究中,在本研究中,在彼此中分化了这两种癌症的诊断的基础。

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