Feature extraction using traditional image processing and convolutional neural network methods to classify white blood cells: a study

Hegde, Roopa B.; Prasad, Keerthana; Hebbar, Harishchandra; Singh, Brij Mohan Kumar

doi:10.1007/s13246-019-00742-9

Cited by 59 publications

(25 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By employing this simple and fast methodology, the possibility of wearable device integration with the algorithm becomes more promising. The suggested technique used a support vector machine (SVM) for classification and used the second fully connected layer as the feature extractor [ 47 , 48 ] (the CNN architecture utilized in this study is described in Table 1 ). AlexNet CNN has been trained with numerous common images, such as cars, boats, planes, dogs, and cats, but it is also possible for the CNN to utilize the distinct properties of non-image data (1D signal)−computationally efficient and locally focused—by converting non-image data into an image, such as a binary image [ 49 ], spectrogram [ 29 , 50 ], recurrence plot [ 32 ], or Gramian Angular Summation Field (GASF) image [ 51 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features

Setiawan

Lin

2021

Brain Sciences

View full text Add to dashboard Cite

A novel identification algorithm using a deep learning approach was developed in this study to classify neurodegenerative diseases (NDDs) based on the vertical ground reaction force (vGRF) signal. The irregularity of NDD vGRF signals caused by gait abnormalities can indicate different force pattern variations compared to a healthy control (HC). The main purpose of this research is to help physicians in the early detection of NDDs, efficient treatment planning, and monitoring of disease progression. The detection algorithm comprises a preprocessing process, a feature transformation process, and a classification process. In the preprocessing process, the five-minute vertical ground reaction force signal was divided into 10, 30, and 60 s successive time windows. In the feature transformation process, the time–domain vGRF signal was modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, feature enhancement with principal component analysis (PCA) was utilized. Finally, a convolutional neural network, as a deep learning classifier, was employed in the classification process of the proposed detection algorithm and evaluated using leave-one-out cross-validation (LOOCV) and k-fold cross-validation (k-fold CV, k = 5). The proposed detection algorithm can effectively differentiate gait patterns based on a time–frequency spectrogram of a vGRF signal between HC subjects and patients with neurodegenerative diseases.

show abstract

Section: Methodsmentioning

confidence: 99%

Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features

Setiawan

Lin

2021

Brain Sciences

View full text Add to dashboard Cite

show abstract

“…Hegde, Roopa B., et al [107] In this paper, a comparative study of traditional learning and CNN based features extraction is organized to classify. The average sensitivity and accuracy of 99% was obtained for of white blood cells classification.…”

Section: Neural Network (R -Cnn)mentioning

confidence: 99%

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

et al. 2021

View full text Add to dashboard Cite

“…CNN is a recognition process that gradually develops from a part to a whole in the aspect of biometric recognition [12]. Compared with the traditional neural networks [13,14], the CNN system shows excellent features such as local receptive fields, shared weights, and multicore convolution. The existence of above advantages can not only greatly reduce the parameters in the neural network but also obtain different levels of characteristic information through the hierarchical network structure.…”

Section: Visual Sensing Technologymentioning

confidence: 99%

Artificial Intelligence‐Assisted Visual Sensing Technology under Duodenoscopy of Gallbladder Stones

Bin

Shen

et al. 2021

Journal of Sensors

View full text Add to dashboard Cite

Objective. This study is aimed at exploring the application effect of duodenoscopy assisted by visual sensing technology based on convolutional neural network (CNN) segmentation algorithm in the diagnosis and treatment of gallbladder stones, so as to provide safer and more effective treatment methods for patients with gallstones. Methods. 188 patients with gallstones and choledocholithiasis who were admitted to our hospital from January 2016 to April 2021 were selected as the research objects. Based on whether the patients were willing to use AI-assisted visual sensing technology during the treatment process, all patients were divided into two groups, namely, the AI group and the conventional group. Various surgical indicators of patients in two groups were compared. Results. The precision, recall, and mean intersection ratio of the M-Unet-based segmentation algorithm were 94.56%, 96.56%, and 98.92%, respectively. In the AI group, the operation time ( 2.74 ± 0.45 h ), postoperative drainage tube placement time ( 4.31 ± 1.15 d ), time required for recovery of gastrointestinal function ( 1.74 ± 0.54 d ), time required to get out of bed ( 1.14 ± 0.55 h ), and time spent in hospital ( 9.94 ± 1.45 d ) were all shorter compared with those in the conventional group, which were 3.21 ± 0.32 h , 12.14 ± 2.98 d , 2.89 ± 0.67 d , 2.09 ± 0.87 h , and 14.14 ± 1.15 h , showing statistical differences ( P < 0.05 ); the intraoperative blood loss ( 79.74 ± 6.45 mL ) and residual status of stones (0%) in the AI group were much lower than those in the conventional group ( P < 0.05 ). In addition, the incidence of complications (10.26%) and the indicators of postoperative gallbladder function of patients in the AI group were lower greatly than those in the conventional group ( P < 0.05 ). Conclusion. The visual sensing technology assisted by the CNN algorithm showed a good effect on image processing, and endoscopic technology can effectively improve the treatment effect of gallbladder stones combined with choledocholithiasis with the aid of this technology. Therefore, the conclusion in this study proved that visual sensing technology based on intelligent algorithms showed a good future in the medical field.

show abstract

Feature extraction using traditional image processing and convolutional neural network methods to classify white blood cells: a study

Cited by 59 publications

References 22 publications

Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features

Identification of Neurodegenerative Diseases Based on Vertical Ground Reaction Force Classification Using Time–Frequency Spectrogram and Deep Learning Neural Network Features

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

Artificial Intelligence‐Assisted Visual Sensing Technology under Duodenoscopy of Gallbladder Stones

Contact Info

Product

Resources

About