Deep Transfer Learning Networks for Brain Tumor Detection: The Effect of MRI Patient Image Augmentation Methods

The exponential growth of deep learning networks has enabled us to handle difficult tasks, even in the complex field of medicine with small datasets. In the sphere of treatment, they are particularly significant. To identify brain tumors, this research examines how three deep learning networks are affected by conventional data augmentation methods, including MobileNetV2, VGG19, and DenseNet201. The findings showed that before and after utilizing approaches, picture augmentation schemes significantly affected the networks. The accuracy of MobileNetV2, which was originally 85.33%, was then enhanced to 96.88%. The accuracy of VGG19, which was 77.33%, was then enhanced to 95.31%, and DenseNet201, which was originally 82.66%, was then enhanced to 93.75%. The models' accuracy percentage engagement change is 13.53%, 23.25%, and 23.25%, respectively. Finally, the conclusion showed that applying data augmentation approaches improves performance, producing models far better than those trained from scratch

Kurdish News Dataset Headlines (KNDH) through multiclass classification

The rapid growth of technology has massively increased the amount of text data. The data can be mined and utilized for numerous natural language processing (NLP) tasks, particularly text classification. The core part of text classification is collecting the data for predicting a good model. This paper collects Kurdish News Dataset Headlines (KNDH) for text classification. The dataset consists of 50000 news headlines which are equally distributed among five classes, with 10000 headlines for each class (Social, Sport, Health, Economic, and Technology). The percentage ratio of getting the channels of headlines is distinct, while the numbers of samples are equal for each category. There are 34 distinct channels that are used to collect the different headlines for each class, such as 8 channels for economics, 14 channels for health, 18 channels for science, 15 channels for social, and 5 channels for sport. The dataset is preprocessed using the Kurdish Language Processing Toolkit (KLPT) for tokenizing, spell-checking, stemming, and preprocessing