Diabetic Retinopathy Diagnosis Dataset
A Comprehensive Dataset for Medical Image Analysis
https://news.1rj.ru/str/datasets1☠️
A Comprehensive Dataset for Medical Image Analysis
https://news.1rj.ru/str/datasets1
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Diabetic Retinopathy Diagnosis Dataset.zip
1.3 GB
Diabetic Retinopathy Diagnosis Dataset
A Comprehensive Dataset for Medical Image Analysis
https://news.1rj.ru/str/datasets1❤️
A Comprehensive Dataset for Medical Image Analysis
https://news.1rj.ru/str/datasets1
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Forwarded from Machine Learning with Python
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Captcha Dataset
Captcha images with solutions of 6 digit numbers
Dataset Structure:
Train : Contains 6,000 images used for training the model.
Test : Contains 2,000 images used for evaluating the model's performance during testing.
Validation : Contains 2,000 images used for validating the model during the training process.
DataFrame:
A CSV file is included with the following columns:
image_path: The relative path to each image file.
solution: The 6-digit number present in the captcha image, which is the label for training and evaluation.
https://news.1rj.ru/str/datasets1🦾
Captcha images with solutions of 6 digit numbers
Dataset Structure:
Train : Contains 6,000 images used for training the model.
Test : Contains 2,000 images used for evaluating the model's performance during testing.
Validation : Contains 2,000 images used for validating the model during the training process.
DataFrame:
A CSV file is included with the following columns:
image_path: The relative path to each image file.
solution: The 6-digit number present in the captcha image, which is the label for training and evaluation.
https://news.1rj.ru/str/datasets1
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Kaggle Data Hub
archive.zip
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Teeth Segmentation on dental X-ray images
The dataset consists of 598 images with a total of 15,318 polygons
About Dataset
Humans in the Loop is excited to publish a new open access dataset for Teeth segmentation on dental radiology scans. The segmentation is done manually by 12 Humans in the Loop trainees in the Democratic Republic of Congo as part of their trainings, using the Panoramic radiography database published by Lopez et al. The dataset consists of 598 images with a total of 15,318 polygons, where each tooth is segmented with a different class.
https://news.1rj.ru/str/datasets1✅
The dataset consists of 598 images with a total of 15,318 polygons
About Dataset
Humans in the Loop is excited to publish a new open access dataset for Teeth segmentation on dental radiology scans. The segmentation is done manually by 12 Humans in the Loop trainees in the Democratic Republic of Congo as part of their trainings, using the Panoramic radiography database published by Lopez et al. The dataset consists of 598 images with a total of 15,318 polygons, where each tooth is segmented with a different class.
https://news.1rj.ru/str/datasets1
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DeepGlobe Road Extraction Dataset
Road Extraction Dataset from DeepGlobe Challenge
Data
The training data for Road Challenge contains 6226 satellite imagery in RGB, size 1024x1024.
The imagery has 50cm pixel resolution, collected by DigitalGlobe's satellite.
The dataset contains 1243 validation and 1101 test images (but no masks).
Label
Each satellite image is paired with a mask image for road labels. The mask is a grayscale image, with white standing for road pixel, and black standing for background.
File names for satellite images and the corresponding mask image are id _sat.jpg and id _mask.png. id is a randomized integer.
Please note:
The values of the mask image may not be pure 0 and 255. When converting to labels, please binarize them at threshold 128.
The labels are not perfect due to the cost for annotating segmentation mask, specially in rural regions. In addition, we intentionally didn't annotate small roads within farmlands.
https://news.1rj.ru/str/datasets1⭐️
Road Extraction Dataset from DeepGlobe Challenge
Data
The training data for Road Challenge contains 6226 satellite imagery in RGB, size 1024x1024.
The imagery has 50cm pixel resolution, collected by DigitalGlobe's satellite.
The dataset contains 1243 validation and 1101 test images (but no masks).
Label
Each satellite image is paired with a mask image for road labels. The mask is a grayscale image, with white standing for road pixel, and black standing for background.
File names for satellite images and the corresponding mask image are id _sat.jpg and id _mask.png. id is a randomized integer.
Please note:
The values of the mask image may not be pure 0 and 255. When converting to labels, please binarize them at threshold 128.
The labels are not perfect due to the cost for annotating segmentation mask, specially in rural regions. In addition, we intentionally didn't annotate small roads within farmlands.
https://news.1rj.ru/str/datasets1
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CT KIDNEY DATASET: Normal-Cyst-Tumor and Stone
Dataset to detect auto Kidney Disease Analysis
Content
The dataset was collected from PACS (Picture archiving and communication system) from different hospitals in Dhaka, Bangladesh where patients were already diagnosed with having a kidney tumor, cyst, normal or stone findings. Both the Coronal and Axial cuts were selected from both contrast and non-contrast studies with protocol for the whole abdomen and urogram. The Dicom study was then carefully selected, one diagnosis at a time, and from those we created a batch of Dicom images of the region of interest for each radiological finding. Following that, we excluded each patient's information and meta data from the Dicom images and converted the Dicom images to a lossless jpg image format. After the conversion, each image finding was again verified by a radiologist and a medical technologist to reconfirm the correctness of the data.
Our created dataset contains 12,446 unique data within it in which the cyst contains 3,709, normal 5,077, stone 1,377, and tumor 2,283
Dataset to detect auto Kidney Disease Analysis
Content
The dataset was collected from PACS (Picture archiving and communication system) from different hospitals in Dhaka, Bangladesh where patients were already diagnosed with having a kidney tumor, cyst, normal or stone findings. Both the Coronal and Axial cuts were selected from both contrast and non-contrast studies with protocol for the whole abdomen and urogram. The Dicom study was then carefully selected, one diagnosis at a time, and from those we created a batch of Dicom images of the region of interest for each radiological finding. Following that, we excluded each patient's information and meta data from the Dicom images and converted the Dicom images to a lossless jpg image format. After the conversion, each image finding was again verified by a radiologist and a medical technologist to reconfirm the correctness of the data.
Our created dataset contains 12,446 unique data within it in which the cyst contains 3,709, normal 5,077, stone 1,377, and tumor 2,283
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ISIC 2019 Skin Lesion images for classification
25,331 images belonging to 8 classes for training models on classification
The dataset for ISIC 2019 contains 25,331 images available for the classification of dermoscopic images among nine different diagnostic categories:
Melanoma
Melanocytic nevus
Basal cell carcinoma
Actinic keratosis
Benign keratosis (solar lentigo / seborrheic keratosis / lichen planus-like keratosis)
Dermatofibroma
Vascular lesion
Squamous cell carcinoma
None of the above
https://news.1rj.ru/str/datasets1🧠
25,331 images belonging to 8 classes for training models on classification
The dataset for ISIC 2019 contains 25,331 images available for the classification of dermoscopic images among nine different diagnostic categories:
Melanoma
Melanocytic nevus
Basal cell carcinoma
Actinic keratosis
Benign keratosis (solar lentigo / seborrheic keratosis / lichen planus-like keratosis)
Dermatofibroma
Vascular lesion
Squamous cell carcinoma
None of the above
https://news.1rj.ru/str/datasets1
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Tuberculosis (TB) Prediction(Top 75 Countries)
About Dataset
This dataset includes 400,000 records with 22 variables that capture demographic, health, and socioeconomic factors influencing tuberculosis incidence across 70 countries. The data is designed to resemble real-world patterns observed in tuberculosis prevalence and healthcare indicators. It can be used for tasks such as denoscriptive analysis, machine learning, and public health research.
https://news.1rj.ru/str/datasets1🏐
About Dataset
This dataset includes 400,000 records with 22 variables that capture demographic, health, and socioeconomic factors influencing tuberculosis incidence across 70 countries. The data is designed to resemble real-world patterns observed in tuberculosis prevalence and healthcare indicators. It can be used for tasks such as denoscriptive analysis, machine learning, and public health research.
https://news.1rj.ru/str/datasets1
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STL-10 Image Recognition Dataset
Train models to recognize different animals and vehicles
Context
STL-10 is an image recognition dataset inspired by CIFAR-10 dataset with some improvements. With a corpus of 100,000 unlabeled images and 500 training images, this dataset is best for developing unsupervised feature learning, deep learning, self-taught learning algorithms. Unlike CIFAR-10, the dataset has a higher resolution which makes it a challenging benchmark for developing more scalable unsupervised learning methods.
Content
Data overview:
There are three files: train_image.zips, test_images.zip and unlabeled_images.zip
10 classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck
Images are 96x96 pixels, color
500 training images (10 pre-defined folds), 800 test images per class
100,000 unlabeled images for unsupervised learning. These examples are extracted from a similar but broader distribution of images. For instance, it contains other types of animals (bears, rabbits, etc.) and vehicles (trains, buses, etc.) in addition to the ones in the labeled set
Images were acquired from labeled examples on ImageNet
https://news.1rj.ru/str/datasets1🆘
Train models to recognize different animals and vehicles
Context
STL-10 is an image recognition dataset inspired by CIFAR-10 dataset with some improvements. With a corpus of 100,000 unlabeled images and 500 training images, this dataset is best for developing unsupervised feature learning, deep learning, self-taught learning algorithms. Unlike CIFAR-10, the dataset has a higher resolution which makes it a challenging benchmark for developing more scalable unsupervised learning methods.
Content
Data overview:
There are three files: train_image.zips, test_images.zip and unlabeled_images.zip
10 classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck
Images are 96x96 pixels, color
500 training images (10 pre-defined folds), 800 test images per class
100,000 unlabeled images for unsupervised learning. These examples are extracted from a similar but broader distribution of images. For instance, it contains other types of animals (bears, rabbits, etc.) and vehicles (trains, buses, etc.) in addition to the ones in the labeled set
Images were acquired from labeled examples on ImageNet
https://news.1rj.ru/str/datasets1
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