Mammogram mastery: A robust dataset for breast cancer detection and medical education.

This data article presents a comprehensive dataset comprising breast cancer images collected from patients, encompassing two distinct sets: one from individuals diagnosed with breast cancer and another from those without the condition. Expert physicians carefully select, verify, and categorize the dataset to guarantee its quality and dependability for use in research and teaching. The dataset, which originates from Sulaymaniyah, Iraq, provides a distinctive viewpoint on the frequency and features of breast cancer in the area. This dataset offers a wealth of information for developing and testing deep learning algorithms for identifying breast cancer, with 745 original images and 9,685 augmented images. The addition of augmented X-rays to the dataset increases its adaptability for algorithm development and instructional projects. This dataset holds immense potential for advancing medical research, aiding in the development of innovative diagnostic tools, and fostering educational opportunities for medical students interested in breast cancer detection and diagnosis.

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.

A vast dataset for Kurdish handwritten digits and isolated characters recognition.

This article presents two massive datasets for central Kurdish handwriting digits and isolated characters named K-ZHMARA and K-PIT. The first dataset, named K-ZHMARA dataset, contains 70,000 images of Kurdish digits, 7000 images for each digit, and a printed A4 paper with a grid of 10 × 10 is used for data collection. Apart from digits, the K-PIT dataset includes 245,000 images of all Kurdish characters, 7000 images for each character; data was collected via a printed A4 paper with a grid of 12 × 10 for this dataset. Moreover, both datasets include 315,000 images. Python programming has been used to scan each piece of paper, segment, crop, resize, binarize, and invert the images via edge detection and image processing techniques.