Convolution Neural Network and Auto-encoder Hybrid Scheme for Automatic Colorization of Grayscale Images

Anitha, A.; Shivakumara, P.; Jain, Shreyansh; Agarwal, Vidhi

doi:10.1007/978-3-031-20541-5_12

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…Initially, details such as the width and height, slice thickness, and resolution of the DICOM image were extracted from the header of the DICOM file [ 28 , 29 ]. Given that the 3D conversion of DICOM images necessitates exclusively retrieving bone-related image information, a blank matrix was populated with 1 based on the origin coordinate axis, aligning with the Threshold set derived from the Hounsfield Unit (HU) range of 1150–1250.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning Model Based on You Only Look Once Algorithm for Detection and Visualization of Fracture Areas in Three-Dimensional Skeletal Images

Jeon,

Kang,

Kuh

et al. 2023

Diagnostics

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Utilizing “You only look once” (YOLO) v4 AI offers valuable support in fracture detection and diagnostic decision-making. The purpose of this study was to help doctors to detect and diagnose fractures more accurately and intuitively, with fewer errors. The data accepted into the backbone are diversified through CSPDarkNet-53. Feature maps are extracted using Spatial Pyramid Pooling and a Path Aggregation Network in the neck part. The head part aggregates and generates the final output. All bounding boxes by the YOLO v4 are mapped onto the 3D reconstructed bone images after being resized to match the same region as shown in the 2D CT images. The YOLO v4-based AI model was evaluated through precision–recall (PR) curves and the intersection over union (IoU). Our proposed system facilitated an intuitive display of the fractured area through a distinctive red mask overlaid on the 3D reconstructed bone images. The high average precision values (>0.60) were reported as 0.71 and 0.81 from the PR curves of the tibia and elbow, respectively. The IoU values were calculated as 0.6327 (tibia) and 0.6638 (elbow). When utilized by orthopedic surgeons in real clinical scenarios, this AI-powered 3D diagnosis support system could enable a quick and accurate trauma diagnosis.

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Section: Methodsmentioning

confidence: 99%

Deep Learning Model Based on You Only Look Once Algorithm for Detection and Visualization of Fracture Areas in Three-Dimensional Skeletal Images

Jeon,

Kang,

Kuh

et al. 2023

Diagnostics

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“…Wang et al [32] proposed a lightweight YOLO-ACG detection algorithm, which balances accuracy and speed and improves the classification errors and missed detection problems in existing steel plate defect detection algorithms. Anitha et al [33] proposed a method that helps in producing vibrant and realistic colors by hybridizing a convolution neural network with an auto-encoder. Chen et al [34] proposed a twostage lightweight detection framework with extremely low computation complexity, which enables high-resolution feature maps for dense anchoring to better cover small objects, proposes a sparsely-connected convolution for computation reduction, enhances the early stage features in the backbone, and addresses the feature misalignment problem for accurate small object detection.…”

Section: Related Workmentioning

confidence: 99%

SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection

Pang

2023

Applied Sciences

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An improved YOLOv8s-based method is proposed to address the challenge of accurately recognizing tiny objects in remote sensing images during practical human-computer interaction. In detecting tiny targets, the accuracy of YOLOv8s is low because the downsampling module of the original YOLOv8s algorithm causes the network to lose fine-grained feature information, and the neck network feature information needs to be sufficiently fused. In this method, the strided convolution module in YOLOv8s is replaced with the SPD-Conv module. By doing so, the feature map undergoes downsampling while preserving fine-grained feature information, thereby improving the learning and expressive capabilities of the network and enhancing recognition accuracy. Meanwhile, the path aggregation network is substituted with the SPANet structure, which facilitates the acquisition of more prosperous gradient paths. This substitution enhances the fusion of feature maps at various scales, reduces model parameters, and further improves detection accuracy. Additionally, it enhances the network’s robustness to complex backgrounds. Experimental verification is conducted on the following two intricate datasets containing tiny objects: AI-TOD and TinyPerson. A comparative analysis with the original YOLOv8s algorithm reveals notable enhancements in recognition accuracy. Specifically, under real-time performance constraints, the proposed method yields a 4.9% and 9.1% improvement in mAP0.5 recognition accuracy for AI-TOD and TinyPerson datasets, respectively. Moreover, the recognition accuracy for mAP0.5:0.95 is enhanced by 3.4% and 3.2% for the same datasets, respectively. The results indicate that the proposed method enables rapid and accurate recognition of tiny objects in complex backgrounds. Furthermore, it demonstrates better recognition precision and stability than other algorithms, such as YOLOv5s and YOLOv8s.

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“…Artificial neural networks (ANNs)-based architectures are usually utilized to reach this goal. PLS-DA, ANN, convolutional neural network (CNN), naïve Bayes, decision trees (DT), k-nearest neighbor (k-NN), k-means clustering, classification and regression tree (CART), boosted regression tree (BRT), random forest (RF), and support vector machines (SVM) are among the most popular techniques used to manage multivariate datasets [34][35][36][37]. Image recognition and classification are the most common applications of ML and DL.…”

Section: Partial Least-squares-discriminant Analysis (Pls-da)mentioning

confidence: 99%

“…Classification models were trained by using preprocessed spectra extracted from ROIs or PCA score clusters. Venetian blinds (VBs) was chosen as cross-validation algorithm in order to assess the optimal complexity of the PLS-DA model [37]. PLS-DA classification performance is usually assessed using statistical parameters calculated from the confusion matrix of the classifier [39].…”

Section: Partial Least-squares-discriminant Analysis (Pls-da)mentioning

confidence: 99%

An Automated Classification of Recycled Aggregates for the Evaluation of Product Standard Compliance

Serranti,

Palmieri,

Bonifazi

et al. 2023

Sustainability

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Nowadays, recycling of construction and demolition waste (C&DW) is a challenging opportunity for the management of such end-of-life (EOL) materials through alternative methods to environmentally unsustainable methods (i.e., landfilling). In order to make recycling processes more effective, quality control systems are needed. In this work, the possibility of developing a sensor-based procedure to recognize different demolition waste materials from a recycling perspective was explored. An automatic recognition of different predefined constituent classes of recyclables (i.e., concrete, mortar, natural stones, unbound aggregates, clay masonry units, bituminous materials) and contaminants (i.e., glass, metals, wood, cardboard, and gypsum plaster), as established by an European standard, was carried out using hyperspectral imaging (HSI) working in the short-wave infrared (SWIR) range (1000–2500 nm). The implemented classification strategies, starting from the collected hyperspectral images of the analyzed constituents, allowed for the identification of the different material categories. Two main models were built for identifying contaminants in recyclable materials and categorizing material groups based on technical specifications. The results showed accurate category identification with Sensitivity and Specificity values over 0.9 in all models. The possibility of performing a full detection of C&DW recycling products can dramatically contribute to increasing the quality of the final marketable products and their commercial value, at the same time reducing the amount of waste and the consumption of primary raw materials.

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Convolution Neural Network and Auto-encoder Hybrid Scheme for Automatic Colorization of Grayscale Images

Cited by 8 publications

References 10 publications

Deep Learning Model Based on You Only Look Once Algorithm for Detection and Visualization of Fracture Areas in Three-Dimensional Skeletal Images

Deep Learning Model Based on You Only Look Once Algorithm for Detection and Visualization of Fracture Areas in Three-Dimensional Skeletal Images

SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection

An Automated Classification of Recycled Aggregates for the Evaluation of Product Standard Compliance

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