Ensuring the security and protection of digital content transmitted over a network is a critical challenge, as data can take various forms, such as text, images, audio, and videos, all of which can be easily manipulat...
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Breast cancer is a major health concern for women worldwide, and early detection is vital to improve treatment outcomes. While existing techniques in mammogram classification have demonstrated promising results, their...
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Breast cancer is a major health concern for women worldwide, and early detection is vital to improve treatment outcomes. While existing techniques in mammogram classification have demonstrated promising results, their limitations become apparent when applied to larger datasets. The decline in performance with increased dataset size highlights the need for further research and advancements in the field to enhance the scalability and generalizability of these techniques. In this study, we propose a framework to classify breast cancer from mammograms using techniques such as mammogram enhancement, discrete cosine transform (DCT) dimensionality reduction, and deep convolutional neural network (DCNN). The first step is to improve the mammogram display to improve the visibility of key features and reduce noise. For this, we use 2-stage Contrast Limited Adaptive Histogram Equalization (CLAHE). DCT is then used to enhance mammograms to reduce residual data. It can provide effective reduction while preserving important diagnostic information. In this way, we reduce the computational complexity and increase the results of subsequent classification algorithms. Finally, DCNN is used on size-reduced DCT coefficients to learn feature discrimination and classification of mammograms. DCNN architectures have been optimized with various techniques to improve their performance, including regularization and hyperparameter tuning. We perform experiments on the DDSM dataset, a large dataset containing approximately 55,000 mammogram images, and demonstrate the effectiveness of the proposed method. We assess the proposed model’s performance by computing the precision, recall, accuracy, F1-Score, and area under the receiver operating characteristic curve (AUC). We achieve Precision and Recall values of 0.929 and 0.963, respectively. The classification accuracy of the proposed models is 0.963. Moreover, the F1-Score and AUC values are 0.962 and 0.987, respectively. These results are better a
Magnesium chips were coated with a high concentration of graphite using a binder and were used as the raw material for injection molding. The microstructure of the magnesium injection-molded product with added graphit...
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Camera-based depth estimation methods are useful for various automobile applications. For example, depth estimation methods based on stereo cameras triangulate the depth between objects and cameras. However, they are ...
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Wheeled mobile mechanisms are essentially unsuitable for moving over rough terrain, but the rocker bogie mechanism is known as a six-wheeled mobile mechanism with high ground adaptability that solves this problem. How...
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Soft actuators have many advantages, such as flexibility and safe interaction with the environment. Despite these advantages, they still lack the stiffness to carry the high load. The layer jamming mechanism can be ap...
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Some Web browsers working on certain operating systems (OSs) specify that a single-word search query entered into the search bar be sent to the recursive resolver of the domain name system (DNS) as a DNS request that ...
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In China, when life insurance companies tried to develop agent channels, they often applied aggressive incentive policies to encourage agents to sell more products and enlarge sales team. Agents can easily utilize imp...
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In recent years, old bridges have increased and require inspection to detect any damaged parts and maintenance to decrease the risk of falling. Bridge inspection robots have developed rapidly to replace human labor. T...
Unbalanced data distribution is a common problem in the development process of credit scoring cards. This paper proposes a new method of mixed sampling, SyMProD-ENN, to improve the problem of developing effective cred...
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