In this study, breast cancer classification as benign or malignant was made using images obtained by histopathological procedures, one of the medical imaging techniques. First of all, different noise types and several intensities were added to the images in the used data set. Then, the noise in images was removed by applying the Wavelet Transform (WT) process to noisy images. The performance rates in the denoising process were found out by evaluating Peak Signal to Noise Rate (PSNR) values of the images. The Gaussian noise type gave better results than other noise types considering PSNR values. The best PSNR values were carried out with the Gaussian noise type. After that, the denoised images were classified by Convolution Neural Network (CNN), one of the deep learning techniques. In this classification process, the proposed CNN model and the VggNet-16 model were used. According to the classification result, better results were obtained with the proposed CNN model than VggNet-16. The best performance (86.9%) was obtained from the data set created Gaussian noise with 0.3 noise intensity.
There has been a significant increase in the use of deep learning algorithms in recent years. Convolutional neural network (CNN), one of the deep learning models, is frequently used in applications to distinguish important objects such as humans and vehicles from other objects, especially in image processing. With the development of image processing hardware, the image processing process is significantly reduced. Thanks to these developments, the performance of studies on deep learning is increasing. In this study, a system based on deep learning has been developed to detect and classify objects (human, car and motorcycle / bicycle) from images captured by drones. Two datasets, the image set of Stanford University and the drone image set created at Afyon Kocatepe University (AKÜ), are used to train and test the deep neural network with the transfer learning method. The precision, recall and f1 score values are evaluated according to the process of determining and classifying human, car and motorcycle / bicycle classes using GoogleNet, VggNet and ResNet50 deep learning algorithms. According to this evaluation result, high performance results are obtained with 0.916 precision, 0.895 recall and 0.906 f1 score value in the ResNet50 model.
Göğüs kanseri dünya genelinde kadınlarda en çok karşılaşılan kanser türüdür. Günümüzde her kadının başına gelebilecek olan göğüs kanseri, erkeklerde de görülebilmektedir. Göğüs kanserinde insanların fiziksel ve zihinsel halleri çok etkilidir. Göğüs kanserine karşın tedbirli olabilmek için belirli aralıklarla göğüs dokularının incelenmesi gerekmektedir. Bu dokular, uzmanlar tarafından incelenmektedir. Ancak inceleme esnasında yapılan yanlış teşhisler tedavi sürecini olumsuz etkilemektedir. Bu sebeple, bu dokuların sayısal ortamda işlenip incelenmesi daha faydalı olmaktadır. Bu çalışmada, YSA ile göğüs kanserinin sınıflandırması yapılmıştır. Mamografi görüntüleri üzerinde Döndürülmüş Yerel İkili Örüntü (RLBP) metodu kullanılarak öznitelikler çıkarılmıştır. Bu öznitelikler, parametreleri belirlenmiş olan YSA aracılığı ile eğitilmiştir. Eğitim sonucunda iyi ve kötü huylu olarak sınıflandırılan ikili sınıflandırmada %87,82 ve Yağlı, Yağlı-Glandüler ve Yoğun-Glandüler olarak sınıflandırılan üçlü arka plan doku sınıflandırmasında %80,95 başarı oranı elde edilmiştir.
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