Lung cancer remains a significant global cause of cancer-related deaths, emphasizing the importance of early detection for improving patient survival rates. This paper introduces an enhanced approach that aims to achi...
Lung cancer remains a significant global cause of cancer-related deaths, emphasizing the importance of early detection for improving patient survival rates. This paper introduces an enhanced approach that aims to achieve efficient and precise lung tumor detection and segmentation. The proposed method utilizes a multimodal approach by leveraging both CT and PET scans, enabling improved tumor detection. The methodology incorporates state-of-the-art deep learning architectures, including ResNet, DenseNet, and Inception-v3, for effective tumor classification. Additionally, both immediate fusion (early fusion) and late fusion techniques are applied to integrate data from multiple modalities. The performance of the classification models is evaluated using metrics such as precision, F1 score, accuracy, and sensitivity. The experimental results demonstrate the effectiveness of the proposed approach in accurately segmenting lung tumors. The findings contribute to the existing knowledge in the field of tumor segmentation and medical image analysis, providing valuable insights into the benefits of multimodal fusion and deep learning techniques for lung cancer diagnosis and treatment planning.
Different types of data sets are established using various sensor-based networks and wearable devices for performing human activity recognition that can be used effectively in the e-health domain. Various machine lear...
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While large language models (LLMs) show promise for various tasks, their performance in compound aspect-based sentiment analysis (ABSA) tasks lags behind fine-tuned models. However, the potential of LLMs fine-tuned fo...
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Spin logics have emerged as a promising avenue for the development of logic-in-memory *** particular,the realization of XOR spin logic gates using a single spin-orbit torque device shows great potential for low-power ...
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Spin logics have emerged as a promising avenue for the development of logic-in-memory *** particular,the realization of XOR spin logic gates using a single spin-orbit torque device shows great potential for low-power stateful logic circuits in the next *** this study,we successfully obtained the XOR logic gate by utilizing a spin-orbit torque device with a lateral interface,which was created by local ion implantation in the Ta/Pt/Co/Ta Hall device exhibiting perpendicular magnetic *** angle of the lateral interface is set at 45°relative to the current direction,leading to the competition between symmetry breaking and current-driven Néel-type domain wall ***,the field-free magnetic switching reversed is realized by the same sign of current amplitude at this *** on this field-free magnetic switching behavior,we successfully proposed an XOR logic gate that could be implemented using only a single spin-orbit torque Hall *** study provides a potentially viable approach toward efficient spin logics and in-memory computing architectures.
The fast development of Large Language Models (LLMs) has made transformative applications in several fields attainable or possible. However, language models must often be more effective in specialized areas, especiall...
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Eye state classification acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so *** modifications in the cognitive levels can be reflected via transforming ...
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Eye state classification acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so *** modifications in the cognitive levels can be reflected via transforming the electro-encephalogram(EEG)*** deep learning(DL)models automated extract the features and often showcased improved outcomes over the conventional clas-sification model in the recognition *** paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classifi-cation(EDLCOA-ESC).The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing ***,wavelet packet decomposition(WPD)technique is employed for the extraction of useful features from the EEG *** addition,an ensemble of deep sparse autoencoder(DSAE)and kernel ridge regression(KRR)models are employed for EEG Eye State classifi***,hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classification results to a maximum *** extensive range of simulation analysis on the benchmark dataset is car-ried out and the results reported the promising performance of the EDLCOA-ESC technique over the recent approaches with maximum accuracy of 98.50%.
Every year, thousands of people throughout the world are diagnosed with skin cancer and enrolled for treatment. Cancer of the skin is one of the forms of cancer that is responsible for the deaths of millions of people...
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ISBN:
(数字)9798331534271
ISBN:
(纸本)9798331534288
Every year, thousands of people throughout the world are diagnosed with skin cancer and enrolled for treatment. Cancer of the skin is one of the forms of cancer that is responsible for the deaths of millions of people every year. The early diagnosis and treatment of newly diagnosed instances of severe skin cancer are very necessary in order to guarantee a high survival rate in addition to a low mortality rate. The custom neural network architecture that has been proposed makes use of a custom CNN for feature extraction and integrates Mobile-Net as its foundation model. For diagnosis, the CNN layers have nine separate classifications. Training and testing the algorithm, the very large ISIC Data Set, which can be accessed via Kaggle, is used. This collection of data contains a large number of images that illustrate the many phases of skin cancer. Based on the findings of this research, the validation and training loss are both rather low, reaching 0.15 and 0.14 respectively. To locate the precise positions of cancer lesions, a CNN-based feature extraction method is used along with a Mobile-Net pre-trained model. Adam optimizer has been implemented with a learning rate of 0.001%, which enables the algorithm to become more effective and learn more efficiently. The accuracy of Mobile-Net models is encouraging enough with a 99.06% training accuracy and a 98.11% of validation accuracy. For further information, this model demonstrates a considerable increase in terms of outcomes for patients and survival rate.
We evaluate five optimization algorithms for laser pulse temporal shape optimization, using a semi-physical model of a high-power laser. Hybrid algorithms combine Differential Evolution and Bayesian optimization algor...
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Abalone is a marine snail found in the cold coastal regions. Age is a vital characteristic that is used to determine its worth. Currently, the only viable solution to determine the age of abalone is through very detai...
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Nonlinear structured illumination microscopy (NSIM) can extend the resolution beyond the 120 nm limit of linear *** combining patterned depletion with rsEGFP2, we achieved 2D-NSIM imaging of live U2OS cells with 75 nm...
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