Neural networks and other advanced imageprocessingalgorithms excel in a wide variety of computer vision and imaging applications, but their high performance also comes at a high computational cost and their success ...
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Skin cancer, which primarily impacts skin exposed to ultraviolet (UV) rays against the sun, represents dangerous to the most significant organs in the human body, the skin. Usually, a spot, lump, or mole that appears ...
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ISBN:
(纸本)9798350377972
Skin cancer, which primarily impacts skin exposed to ultraviolet (UV) rays against the sun, represents dangerous to the most significant organs in the human body, the skin. Usually, a spot, lump, or mole that appears on the skin is the primary suspicion of skin cancer. However, each of these can undergo changes in coloring or shape as time passes. Recovery for skin cancer is mostly possible if the disease is discovered early. Numerous medical diagnostic methods, such as Dermoscopy, biopsy, and ocular examination of the affected area, are useful in helping anticipate the development of skin cancer. However, these approaches have the disadvantage of delivering erroneous results because it is extremely difficult to distinguish between normal and malignant skin. Therefore, the drawback of these diagnostic procedures is that machine learning algorithms are currently used together with imageprocessing techniques to examine the images for the purpose of precisely identifying skin cancer. The current research employs the ISIC dataset to develop a novel model for skin cancer classification that combines imageprocessing techniques with advanced machine learning methods, including Crammer-Singer Support vector machine learning algorithms. The categorization of skin cancer begins with preprocessing the input image, which includes hair removal using a morphological filter and image enhancement using a median filter to minimize noise and increase image clarity. The ABCD approach is used to segment lesion images by evaluating them for asymmetry, border irregularity, color variability, and diameter. The crammer-Singer SVM algorithm is then used with these images to classify skin lesions into various types such as melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), and squamous cell carcinoma (SCC), leveraging its robust multi-class handling capabilities. The system achieve
With the continuous advancement of smart city construction, autonomous driving technology is playing an increasingly important role in urban traffic systems. This study aims to explore the development and optimization...
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Deep Learning is a technology developed with GPU Acceleration that has a good ability to process image computations. One of the deep learning methods widely used in classifying two-dimensional objects is the Convoluti...
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ISBN:
(纸本)9783031214370;9783031214387
Deep Learning is a technology developed with GPU Acceleration that has a good ability to process image computations. One of the deep learning methods widely used in classifying two-dimensional objects is the Convolutional Neural Network algorithm. Just like other imageprocessingalgorithms, the classification process is very dependent on the quality of the image used. Therefore, it is concerned that pre-processing is done. This study aimed to find a scenario for image data pre-processing by comparing the threshold types used. By using two scenarios, the first scenario using Simple Threshold and the second scenario using threshold Canny. The first scenario begins with collecting data from an X-ray image after the established dataset is advanced to pre-process the data set. In this pre-processing data, several things were done to increase the level of data accuracy by changing and equalizing the pixel size in the dataset, changing the color of the image on the dataset to grayscale, distributing the histogram or commonly known as histogram equalization, and finally applying a simple threshold. Unlike the second scenario, which does not use a simple threshold but uses a threshold canny. After completion of the pre-processing stage and then the continued training phase. At this stage, the dataset will be trained using CNN. After the dataset is trained, it enters the testing stage. The testing stage shows the results that the data is classified properly. The validation obtained from the two scenarios shows that the simple threshold gives better results than the canny threshold, with a value that shows a simple threshold of 97% and a canny threshold of 89%. This result shows that the dataset's treatment differences greatly affect the results' accuracy.
In the field of agriculture being able to identify plant diseases is extremely important as it can lead to crop loss and negatively impact food security. Detecting these diseases early on is crucial, to prevent their ...
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This study explores the application of computer vision technology to improve the efficiency of maintaining cleanliness and order in high-traffic commercial areas. Given the growing importance of automation in managing...
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The leading cause of visual impairment after cataract, is glaucoma and the only way to combat it is to detect it early. It is imperative to develop a system that can work effectively without a lot of equipment, qualif...
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L Color cast, an aberration common in digital images, poses challenges in various imageprocessing applications, affecting image quality and visual perception. This research investigates diverse methodologies for colo...
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With the rapid development of big data and Internet of Things (IoT), more and more digital products are emerging. However, this has also brought about a growing problem of copyright violation. Digital image robust wat...
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作者:
Seitaj, OltianaMechanics
University of Roma Tre Department of Industrial Engineering Electronics Rome Italy
This paper evaluates the impact of hybrid deep learning approaches on lung tumor segmentation by combining traditional imageprocessing techniques with advanced AI-driven models. The study integrates Convolutional Neu...
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