Climate Change: Earth Surface Temperature Data

https://news.1rj.ru/str/datasets1 ⭐️

Wheat Leaf dataset

disease affected and healthy wheat leaf

Size: 1.52 GB

The data contains 102 healthy, 208 stripe rust, and 97 septoria detected wheat leaf.

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Wheat Leaf dataset

https://news.1rj.ru/str/datasets1 ✅

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Eye-dataset
Eye seeing in that direction

Context:
The image data was gathered for automating an wheelchair. It can controlled by direction given by eye.

Content:
The image dataset consists of four folder and comprising a total of 14.5k images.
The images in the dataset are regularized and augmented so that we can achieve more than 96% accuracy on the first training session.

https://news.1rj.ru/str/datasets1 👍

Eye-dataset
Eye seeing in that direction

https://news.1rj.ru/str/datasets1 💫

Handwriting Recognition

Trannoscriptions of 400,000 handwritten names

About Dataset
Overview
This dataset consists of more than four hundred thousand handwritten names collected through charity projects.

Character Recognition utilizes image processing technologies to convert characters on scanned documents into digital forms. It typically performs well in machine-printed fonts. However, it still poses difficult challenges for machines to recognize handwritten characters, because of the huge variation in individual writing styles.

There are 206,799 first names and 207,024 surnames in total. The data was divided into a training set (331,059), testing set (41,382), and validation set (41,382) respectively.

Content
The input data here are hundreds of thousands of images of handwritten names. In the Data, you’ll find the transcribed images broken up into test, training, and validation sets.

Image Lable follow the following naming format enabling you to extend the data set with your own data.

https://news.1rj.ru/str/datasets1 ⭐️

Handwriting Recognition

Trannoscriptions of 400,000 handwritten names

https://news.1rj.ru/str/datasets1 💫

Arabic Handwritten Characters Dataset

Content
The data-set is composed of 16,800 characters written by 60 participants, the age range is between 19 to 40 years, and 90% of participants are right-hand. Each participant wrote each character (from ’alef’ to ’yeh’) ten times on two forms as shown in Fig. 7(a) & 7(b). The forms were scanned at the resolution of 300 dpi. Each block is segmented automatically using Matlab 2016a to determining the coordinates for each block. The database is partitioned into two sets: a training set (13,440 characters to 480 images per class) and a test set (3,360 characters to 120 images per class). Writers of training set and test set are exclusive. Ordering of including writers to test set are randomized to make sure that writers of test set are not from a single institution (to ensure variability of the test set).

In an experimental section we showed that the results were promising with 94.9% classification accuracy rate on testing images. In future work, we plan to work on improving the performance of handwritten Arabic character recognition.

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Arabic Handwritten Characters Dataset ✋

https://news.1rj.ru/str/datasets1💫

Skin-Disease-Dataset

skin infections, such as bacteria, viruses, fungi, and parasites.

About Dataset
Some types of pathogens — notably bacteria and fungi — are typically present on the skin, but if they become too numerous, the immune system can no longer manage them.In this case, an infection can result.The cause of a skin infection depends on the pathogen involved.
In this dataset image collected from internet.

this dataset there are total 8 class ,They are
Bacterial Infections- cellulitis
Bacterial Infections- impetigo
Fungal Infections - athlete -foot
Fungal Infections - nail-fungus
Fungal Infections - ringworm
Parasitic Infections - cutaneous-larva-migrans
Viral skin infections - chickenpox
Viral skin infections - shingles

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Skin-Disease-Dataset

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OASIS Alzheimer's Detection

Large-scale brain MRI dataset for deep neural network analysis

About Dataset
The dataset used is the OASIS MRI dataset (https://sites.wustl.edu/oasisbrains/), which consists of 80,000 brain MRI images. The images have been divided into four classes based on Alzheimer's progression. The dataset aims to provide a valuable resource for analyzing and detecting early signs of Alzheimer's disease.

To make the dataset accessible, the original .img and .hdr files were converted into Nifti format (.nii) using FSL (FMRIB Software Library). The converted MRI images of 461 patients have been uploaded to a GitHub repository, which can be accessed in multiple parts.
For the neural network training, 2D images were used as input. The brain images were sliced along the z-axis into 256 pieces, and slices ranging from 100 to 160 were selected from each patient. This approach resulted in a comprehensive dataset for analysis.

Patient classification was performed based on the provided metadata and Clinical Dementia Rating (CDR) values, resulting in four classes: demented, very mild demented, mild demented, and non-demented. These classes enable the detection and study of different stages of Alzheimer's disease progression.

During the dataset preparation, the .nii MRI scans were converted to .jpg files. Although this conversion presented some challenges, the files were successfully processed using appropriate tools. The resulting dataset size is 1.3 GB.

https://news.1rj.ru/str/datasets1 🌟

OASIS Alzheimer's Detection

https://news.1rj.ru/str/datasets1 🏳