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In this study, a study was conducted with the HAM10000 dataset containing skin lesions to see the performance of architectures such as EfficienNetB3, which uses deep learning techniques to detect skin cancer. In this study, applications were made in deep learning to see the effect of the learning rate or learning rate on architectures using convolutional neural networks used in image classification. These applications were made to see the effect of the learning rate, the size and diversity of the data set, and the number of images used in training, as well as the increased number of images, on both the success in classification and the time taken for training. EfficientNetB3 was used as the architecture and HAM1000 dataset, which is open access on the Kaggle platform, was used as the dataset. At the end of the study, without using as many augmented images as possible and with 600 images of each disease class, a learning rate of 0.002 and epoch 15 instead of 10, the highest accuracy performance of 0.8234 was achieved in the fifth application. [ABSTRACT FROM AUTHOR]

Lung cancer is a common health problem in our country and in the world, and it ranks first in cancer-related deaths. Early diagnosis of lung cancer is of vital importance in terms of more informed progress about the course of the disease and survival of the patient. Recently, with the development of technology, artificial intelligence and deep learning-based systems; by using data obtained from medical imaging systems such as Computed Tomography (CT), Magnetic Resonance (MR), it provides some convenience to experts to diagnose the disease. In this study, a new Convolutional Neural Network (CNN) model is proposed to classify cancerous and normal lung CT images. The classification results of the proposed ESA model and the ResNeXt deep learning model, which is more successful than other pre-trained deep learning models in the literature, were compared. As a result of the study, it has been observed that the proposed ESA model performs better than the ResNeXt architecture, with an accuracy of 99%. In addition, the images in the current study were classified in their raw form without using any feature extraction method. And it has been observed that the proposed ESA model has a lower number of layers compared to the methods used in similar studies in the literature, as well as a higher classification success. [ABSTRACT FROM AUTHOR]