Deep Learning based Approach for Bone Diagnosis Classification in Ultrasonic Computed Tomographic Images

Fradi, Marwa; Afif, Mouna; Machhout, Mohsen

doi:10.14569/ijacsa.2020.0111210

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…As [6] artificial intelligence in medicine shows that ultrasonic imaging technologies have a true diagnosis; Two types of neural network algorithms have been proposed in three categories: USCT images of healthy, fractured and osteoporotic bones. Initially, a Convolutional Neural Network classifier system is presented and then an evolutionary neural network with the AmeobaNet model for the USCT images www.ijacsa.thesai.org classification.…”

Section: Related Workmentioning

confidence: 99%

Analogy of the Application of Clustering and K-Means Techniques for the Approximation of Values of Human Development Indicators

Rocha¹,

Zela²,

Torres³

et al. 2021

IJACSA

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The objective of this study was to apply Clustering and K-Means' techniques to classify the departments of Peru according to their Human Development Index. In this article, the elbow method was used to determine the optimal number of clusters, applying the classification algorithms to group the departments of Peru according to their similarities, in addition to the Principal Component Analysis (PCA) technique for a better display of clusters. After applying the unsupervised algorithms, the results were more relevant in clusters 2 and 4 according to their HDI, made up of the departments of Arequipa, the Constitutional Province of Callao, Ica, Lima, Moquegua and Tacna, where the most notable is the life expectancy at birth, the population with full secondary education, the number of years of education, the average per capita income, and the state's density index. The results obtained by the K-Means algorithm show more cohesive results than the Clustering algorithm.

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Section: Related Workmentioning

confidence: 99%

Analogy of the Application of Clustering and K-Means Techniques for the Approximation of Values of Human Development Indicators

Rocha¹,

Zela²,

Torres³

et al. 2021

IJACSA

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show abstract

“…Using deep learning and especially deep convolutional neural networks-based architectures, computer vision tasks have been known a great improvements and achievements. Depp learning-based technique have employed for different applications including Alzheimer disease classification [30], gesture recognition [31], rice disease detection [32], facial expression recognition [33], places classification [34] and medical imaging [35].…”

Section: Related Workmentioning

confidence: 99%

A Transfer Learning Approach for Indoor Object Identification

et al. 2021

Self Cite

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Accessing new indoor environments is a well-known challenge for blind and visually impaired persons (VIP). In this work, we propose a new computer vision-based indoor object recognition system used especially for indoor wayfinding and indoor assistance navigation. The proposed recognition system is based on transfer learning techniques. This system is able to detect with a big performance three categories of indoor classes (door, stairs and sign). We developed an efficient and a robust indoor landmark identification system based on a lightweight deep convolutional neural network (DCNN). The proposed detection system is generic and performant enough to handle the large intra-class variation provided in the training and the testing sets. Experimental results have shown the big efficiency of the obtained systems by achieving high recognition rates.

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“…Recently, deep learning-based architecture achieved breakthrough success in various arti cial intelligence applications including: objects detection [2], indoor objects detection [3,4], way nding assistance navigation [5], indoor objects recognition [6], indoor objects recognition applied for IoT [7], drivers fatigue detection [8], road sign detection [9] and medical imaging processing [10].…”

Section: Introductionmentioning

confidence: 99%

Deep Embedded Lightweight CNN Network for Indoor Objects Detection on FPGA

Afif

Ayachi

Yahia

et al. 2023

Preprint

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indoor objects detection and recognition present an active research axis in computer vision and artificial intelligence fields. Various deep learning-based techniques can be applied to solve the objects detection problems. With the apperance of deep convolutional neural networks (DCNN) a great breakthrough for various applications was achieved. Indoor objects detection present a primary task that can assist blind and visually impaired persons (BVI) during their navigation. However, building a reliable indoor objects detection system used for edge devices implementations still presents a serious challenge. To address this problem, we propose in this work to build an indoor objects detection system based on DCNN network. Cross-stage partial network (CSPNet) has been used for the detection process and a lightweight backbone based on EfficientNet v2was used as a network backbone. In order to ensure a lightweight implementation of the proposed work on FPGA devices, various optimizations techniques have been applied to compress the model size and reduce its computation complexity. The proposed indoor objects detection system was implemented on a Xilinx ZCU 102 board. Training and testing experiments have been conducted on the proposed indoor objects dataset that count 11000 images containing 25 landmark classes. The proposed work achieved very competitive results in terms of detection accuracy and processing time for the original CSP-EfficientNet v2 network as well as for the proposed compressed version.

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Deep Learning based Approach for Bone Diagnosis Classification in Ultrasonic Computed Tomographic Images

Cited by 8 publications

References 25 publications

Analogy of the Application of Clustering and K-Means Techniques for the Approximation of Values of Human Development Indicators

Analogy of the Application of Clustering and K-Means Techniques for the Approximation of Values of Human Development Indicators

A Transfer Learning Approach for Indoor Object Identification

Deep Embedded Lightweight CNN Network for Indoor Objects Detection on FPGA

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