Retracted: Image Recognition Technology Based on Machine Learning

Liu, Lijuan; Wang, Yanping; Chi, Wanle

doi:10.1109/access.2020.3021590

Cited by 37 publications

(16 citation statements)

References 30 publications

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“…A study [ 8 ] has similar accuracy for classification, but they have also done segmentation using the watershed segmentation method and got an mAP of 90%. A study [ 15 ] got the lowest segmentation mAP of 89% and the highest classification accuracy of 94%. Finally, our research has higher accuracy than other studies for both segmentation and classification models.…”

Section: Results and Analysismentioning

confidence: 99%

“…Liu et al [ 15 ] worked on machine learning technology for image recognition in which they found transportation industry picture acknowledgment innovation is applied to tag acknowledgment to extricate tags from the complex foundation, section tag characters, and build an AI nontag programmed age calculation, which might work on the proficiency of nontag acknowledgment. The variety and high age-speed of tag-preparing test sets can accomplish the motivation behind successfully preparing solid classifiers.…”

Section: Literature Reviewmentioning

confidence: 99%

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Towards the Segmentation and Classification of White Blood Cell Cancer Using Hybrid Mask-Recurrent Neural Network and Transfer Learning

Das

Islam

Neha

et al. 2021

Contrast Media & Molecular Imaging

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Inside the bone marrow, plasma cells are created, and they are a type of white blood cells. They are made from B lymphocytes. Antigens are produced by plasma cells to combat bacteria and viruses and prevent inflammation and illness. Multiple myeloma is a plasma cell cancer that starts in the bone marrow and causes the formation of abnormal plasma cells. Multiple myeloma is firmly identified by examining bone marrow samples under a microscope for myeloma cells. To diagnose myeloma cells, pathologists have to be very selective. Furthermore, because the ultimate decision is based on human sight and opinion, there is a possibility of error in the result. The nobility of this research is that it provides a computer-assisted technique for recognizing and detecting myeloma cells in bone marrow smears. For recognizing purposes, we have used Mask-Recurrent Convolutional Neural Network, and for detection purposes, Efficient Net B3 has been used. There are already many studies on white blood cell cancer, but very few with both segmentation and classification. We have designed two models. One is for recognizing myeloma cells, and the other is for differentiating them from nonmyeloma cells. Also, a new data set has been made from the multiple myeloma data sets, which has been used in our classification model. This research focuses on hybrid segmentation models and increases the accuracy level of the classification model. Both of our models are trained pretty well, where the Mask-RCNN model gives a mean average precision (mAP) of 93% and the Efficient Net B3 model gives 94.68% accuracy. The result of this research indicates that the Mask-RCNN model can recognize multiple myeloma and Efficient Net B3 can distinguish between myeloma and nonmyeloma cells and beats most of the state of the art in myeloma recognition and detection.

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Section: Results and Analysismentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Towards the Segmentation and Classification of White Blood Cell Cancer Using Hybrid Mask-Recurrent Neural Network and Transfer Learning

Das

Islam

Neha

et al. 2021

Contrast Media & Molecular Imaging

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“…Intelligence is based on the concept of analyzing data using digital images that can be processed using a computer and then extracting data from them [ 15 ]. Martinez et al illustrated low-dose CT images for detecting myeloma in the bone marrow [ 16 ]. Xu et al worked on image classification as well as the type and position of objects [ 17 ], VGG [ 18 ], inception [ 19 ], fuzzy logic [ 20 ], Faster R-CNN [ 21 ], SSD [ 19 ], and YOLO [ 11 ], which are all methods for segmentation and object detection.…”

Section: Literature Reviewmentioning

confidence: 99%

“…is section covers the research articles of various researchers who worked on the method of detecting cancer cells in WBCs by using an image processing algorithm to detect myeloma, which is developed as discussed in [13] Intelligence is based on the concept of analyzing data using digital images that can be processed using a computer and then extracting data from them [15]. Martinez et al illustrated low-dose CT images for detecting myeloma in the bone marrow [16]. Xu et al worked on image classification as well as the type and position of objects [17], VGG [18], inception [19], fuzzy logic [20], Faster R-CNN [21], SSD [19], and YOLO [11], which are all methods for segmentation and object detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Segmentation and Classification of White Blood Cells Using the UNet

Alharbi

Aravinda²,

Meng

et al. 2022

Contrast Media & Molecular Imaging

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In the bone marrow, plasma cells are made up of B lymphocytes and are a type of WBC. These plasma cells produce antibodies that help to keep bacteria and viruses at bay, thus preventing inflammation. This presents a major challenge for segmenting blood cells, since numerous image processing methods are used before segmentation to enhance image quality. White blood cells can be analyzed by a pathologist with the aid of computer software to identify blood diseases accurately and early. This study proposes a novel model that uses the ResNet and UNet networks to extract features and then segments leukocytes from blood samples. Based on the experimental results, this model appears to perform well, which suggests it is an appropriate tool for the analysis of hematology data. By evaluating the model using three datasets consisting of three different types of WBC, a cross-validation technique was applied to assess it based on the publicly available dataset. The overall segmentation accuracy of the proposed model was around 96%, which proved that the model was better than previous approaches, such as DeepLabV3+ and ResNet-50.

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“…The image recognition process in computer science is known as computer vision, which can be applied to agriculture in identifying plant diseases [10] [11]. Machine learning techniques that are commonly used in image identification [12] must begin with obtaining image data that does not contain a lot of noise (background) to maintain good identification results [13]. In this case, image segmentation is an important part of image processing techniques in several studies for image identification [14] [15].…”

Section: Introductionmentioning

confidence: 99%

KNN Algorithm for Identification of Tomato Disease Based on Image Segmentation Using Enhanced K-Means Clustering

Nasution

Alvin²,

Siregar³

et al. 2022

KINETIK

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Image segmentation is an important process in identifying tomato diseases. The technique that is often used in this segmentation is k-means clustering. One of the main problems in this technique is the case of local minima, where the cluster that is formed is not suitable due to the incorrect selection of the initial centroid. In image data, this case will have an impact on poor segmentation results because it can erase parts that are actually important to be lost or there is still background in the recognition process, which has an impact on decreasing accuracy results. In this research, a method for image segmentation will be proposed using the k-means clustering algorithm, which has been added with the cosine similarity method as the proposed contribution. The use of the cosine method will determine the initial centroid by calculating the level of similarity of each image feature based on color and dividing them into several categories (low, medium, and high values). Based on the results obtained, the proposed algorithm is able to segment and distinguish between leaf and background images with good results, with the kNN reaching a value of 94.90% for accuracy, 99.50% for sensitivity, and 93.75% for specificity. The results obtained using the kNN method with k-means segmentation obtained a value of 92.46% for accuracy, 96.30% for sensitivity, and 91.50% for specificity. The results obtained using the kNN method without segmentation obtained a value of 90.22% for accuracy, 93.30% for sensitivity, and 89.45% for specificity.

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Retracted: Image Recognition Technology Based on Machine Learning

Cited by 37 publications

References 30 publications

Towards the Segmentation and Classification of White Blood Cell Cancer Using Hybrid Mask-Recurrent Neural Network and Transfer Learning

Towards the Segmentation and Classification of White Blood Cell Cancer Using Hybrid Mask-Recurrent Neural Network and Transfer Learning

Segmentation and Classification of White Blood Cells Using the UNet

KNN Algorithm for Identification of Tomato Disease Based on Image Segmentation Using Enhanced K-Means Clustering

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