Deep learning model for thorax diseases detection

Shadeed, Ghada A.; Tawfeeq, Mohammed Ali; Mahmoud, Sawsan M.

doi:10.12928/telkomnika.v18i1.12997

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…The building of mobile models is based on enhancing the efficiency for the building blocks [22]. MobileNetV1 [23] presented depthwise separable convolutions (DSC) as an efficient change for other CNN layers.…”

Section: The Proposed Modelmentioning

confidence: 99%

The detection of handguns from live-video in real-time based on deep learning

2020

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Many people have been killed indiscriminately by the use of handguns in different countries. Terrorist acts, online fighting games and mentally disturbed people are considered the common reasons for these crimes. A realtime handguns detection surveillance system is built to overcome these bad acts, based on convolutional neural networks (CNNs). This method is focused on the detection of different weapons, such as (handgun and rifles). The identification of handguns from surveillance cameras and images requires monitoring by human supervisor, that can cause errors. To overcome this issue, the designed detection system sends an alert message to the supervisor when a weapon is detected. In the proposed detection system, a pre-trained deep learning model MobileNetV3-SSDLite is used to perform the handgun detection operation. This model has been selected because it is fast and accurate in infering to integrate network for detecting and classifying weapons in images. The experimental result using global handguns datasets of various weapons showed that the use of MobileNetV3 with SSDLite model both enhance the accuracy level in identifying the real time handguns detection.

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Section: The Proposed Modelmentioning

confidence: 99%

The detection of handguns from live-video in real-time based on deep learning

2020

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“…11, No. 2, June 2022: 485-493 486 same performance as the best experts in diagnosing diseases based on medical images at a low cost at any time [5].…”

Section: Introductionmentioning

confidence: 99%

A proposed architecture for convolutional neural networks to detect skin cancers

Ahmed

Kashmola

2022

IJ-AI

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The goal of the research paper is to design and development of a computer-based system for the segmentation and classification of malignant skin diseases and a comparison between the accuracy of their detection, as two malignant diseases of skin diseases were detected. Namely, basal cell carcinoma and melanoma separately with images of nevus, and the images were collected from the ISIC 2020 archive group, as the total, The images used: 17,846 images include 3,008 images of basal cell carcinoma (BCC), 5,272 images of melanoma, and 9,566 images of a nevus, and validation data contains 20% of the images used which are not classified and randomly taken from the set of images, and the final test data contains 1,500 anonymous images. An architecture for the convolutional neural network technology in deep learning has been proposed that consists of a set of layers for processing. Processing raw input images for a group of pre-treatment transformations, the data augmentation process, so the number of images used became 86094 images of nevus, 27,072 images of BCC, and 47,448 images of melanoma. Through the detection process, the classification and detection accuracy of BCC was 98.25%, which is higher than the classification accuracy of melanoma is 91.61%.

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“…Furthermore, Sun et al proposed two deep neural network architectures, DeepID3, and achieved 99.53% accuracy in LFW face verification and 96.0% LFW rank-1 face identification [17]. In addition, CNNs have been used for medical image analysis [18,19]. In 1994, the CNNs were used to detect the micro-calcifications in digital mammography [20].…”

Section: Introductionmentioning

confidence: 99%

Searching surveillance video contents using convolutional neural network

Mohammad

Aljarrah

Jarrah

2021

IJECE

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Manual video inspection, searching, and analyzing is exhausting and inefficient. This paper presents an intelligent system to search surveillance video contents using deep learning. The proposed system reduced the amount of work that is needed to perform video searching and improved the speed and accuracy. A pre-trained VGG-16 CNNs model is used for dataset training. In addition, key frames of videos were extracted in order to save space, reduce the amount of work, and reduce the execution time. The extracted key frames were processed using the sobel operator edge detector and the max-pooling in order to eliminate redundancy. This increases compaction and avoids similarities between extracted frames. A text file, that contains key frame index, time of occurrence, and the classification of the VGG-16 model, is produced. The text file enables humans to easily search for objects of interest. VIRAT and IVY LAB datasets were used in the experiments. In addition, 128 different classes were identified in the datasets. The classes represent important objects for surveillance systems. However, users can identify other classes and utilize the proposed methodology. Experiments and evaluation showed that the proposed system outperformed existing methods in an order of magnitude. The system achieved the best results in speed while providing a high accuracy in classification.

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Deep learning model for thorax diseases detection

Cited by 13 publications

References 15 publications

The detection of handguns from live-video in real-time based on deep learning

The detection of handguns from live-video in real-time based on deep learning

A proposed architecture for convolutional neural networks to detect skin cancers

Searching surveillance video contents using convolutional neural network

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