Prediction of Melanoma from Dermoscopic Images Using Deep Learning-Based Artificial Intelligence Techniques

Kaplan, Ali; Güldoğan, Emek; Çolak, Cemil; Arslan, Ahmet

doi:10.1109/idap.2019.8875970

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…To eliminate the adverse effects caused by singular sample data, we can use the MinMaxScaler function to normalize the data. That is, the data X is first centered at the minimum value and then scaled by the range (maximum–minimum) [ 24 , 25 , 26 ]. At this point, the data converges between [0, 1].…”

Section: Methodsmentioning

confidence: 99%

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

Zhao

Wang

et al. 2023

Micromachines

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Single-event effects (SEE) are an important index of radiation resistance for fully depleted silicon on insulator (FDSOI) devices. The research into traditional FDSOI devices is based on simulation software, which is time consuming, requires a large amount of calculation, and has complex operations. In this paper, a prediction method for the SEE of FDSOI devices based on deep learning is proposed. The characterization parameters of SEE can be obtained quickly and accurately by inputting different particle incident conditions. The goodness of fit of the network curve for predicting drain transient current pulses can reach 0.996, and the accuracy of predicting the peak value of drain transient current and total collected charge can reach 94.00% and 96.95%, respectively. Compared with TCAD Sentaurus software, the simulation speed is increased by 5.10 × 102 and 1.38 × 103 times, respectively. This method can significantly reduce the computational cost, improve the simulation speed, and provide a new feasible method for the study of the single-event effect in FDSOI devices.

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Section: Methodsmentioning

confidence: 99%

Prediction of Single-Event Effects in FDSOI Devices Based on Deep Learning

Zhao

Wang

et al. 2023

Micromachines

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“…In study of Kaplan et al [9], clinical decision support systems that assist healthcare personnel in inpatient evaluations are obtained with the VGG16 deep learning model. In line with the study, revealed model support to physicians in the medical decision-making process.…”

Section: Related Workmentioning

confidence: 99%

Classification and Diagnosis of Mammary Tumors in Dogs Using Deep Learning Techniques

Işık,

Özmen,

Eskicioğlu

et al. 2023

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Mammary tumors are commonly found in domestic animals and significantly affect animal health. In this context, the analysis of mammary tumor cells in domestic animals is crucial for the diagnosis, monitoring, and treatment of cancer. These tumors can also lead to the death of animals. Therefore, academic studies are needed to prevent and early diagnose mammary cancer in animals. In this study, an artificial intelligence-supported system for early diagnosis of whether mammary tumor sections taken from domestic dogs will cause cancer was developed. The proposed system consists of image processing and classification stages. The data for the study was provided by the Faculty of Veterinary Medicine at Burdur Mehmet Akif Ersoy University. In the image processing stage, various data preprocessing techniques and deep learning-based convolutional neural networks were used. Then, benign and malignant mammary tumors were classified to diagnose cancer. Data preprocessing techniques such as data augmentation, normalization and image filtering techniques were used to improve the performance of the deep learning-based image processing algorithm. The Xception model achieved a 98.9% success rate in tumor diagnosis. The study is believed to have made a significant contribution to the literature by using current methods and having a unique data set.

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