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This robust dataset is extracted from the International Skin Imaging Collaboration (ISIC). Similar datasets are used for the annual ISIC Challenge, presenting an opportunity for the computer science community to produce algorithms that can outperform professional dermatology. The submitted dataset contains approximately 1,000 images of malignant melanomas, as well as approximately 1,000 images of benign melanomas.
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The Autofocus Projector Dataset is a collection of 555 images and 150 videos captured while projecting images and videos with varying levels of Gaussian blur. The dataset includes images and videos of different blur levels, ranging from fully focused to the maximum levels of left and right Gaussian blur as per the projector's specifications. The dataset was recorded using a Redmi Note 11T 5G mobile camera with a 50 MP, f/1.8, 26mm (wide) sensor, PDAF image camera, and 1080p@30 fps video camera.
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Ovarian cancer is among the top health issues faced by women everywhere in the world . Ovarian tumours have a wide range of possible causes. Detecting and tracking down these cancers in their early stages is difficult which adds to the difficulty of treatment. In most cases, a woman finds out she has ovarian cancer after it has already spread. In addition, as technology in the field of artificial intelligence advances, detection can be done at an earlier level. Having this data will assist the gynaecologist in treating these tumours as soon as possible.
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One of the most consequential creations in the human evolution phase is handwriting. Due to writing, today we are conveying our reflections, making business pacts, rendering an understandable world and making hitherto tasks austerer. Determining gender using offline handwriting is an applied research problem in forensics, psychology, and security applications, and with technological evolution, the need is growing. The general problem of gender detection from handwriting poses many difficulties resulting from interpersonal and intrapersonal differences.
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We created a 2563-image custom dragon fruit image dataset, with 1248 images of raw dragon fruits and 1315 photographs of ripe dragon fruits. The images were taken with the Nikon D5200 DSLR and OnePlus 6's Sony IMX 519 16 megapixel camera. The photographs taken with the DSLR camera had a resolution of 4000 by 6000 pixels, while those taken with the OnePlus6 had a resolution of 3456 by 4608 pixels. They were photographed in natural sunlight. The average temperature during that time was 28°C (84.2°F), with partly sunny skies, 65 percent humidity, and 17 km/h wind speeds.
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2719 Views
Currency recognition and classification is one essential task to do. Both paper and coin currency play important role in transactions in everyday life. But provided there are many datasets available of paper currency, and very less datasets are available of coin currency. Coin currency recognition becomes important because even though the amount for which people do coin transactions is small but inaccuracy in recognition can lead to huge loss. Following are the objectives to create this dataset:
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This dataset is used for the interference detection in high frequency over-the-horizon (OTH) radar, especially the sky-wave mode. The range-Doppler (RD) grey images are categorized into three types, i.e. the radio frequency interference (RFI), the transient interference (TSI), and no interference (NoI). Two databases are included. The simulated database is constructed based on signal models of interference, clutter and noise. The real database is constructed based on real data from OTH radars.
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