Parameter Optimization of Local Fuzzy Patterns Based on Fuzzy Contrast Measure for White Blood Cell Texture Feature Extraction

Fatichah, Chastine; Tangel, Martin Leonard; Widyanto, M. Rahmat; Dong, Fangyan; Hirota, Kaoru

doi:10.20965/jaciii.2012.p0412

Cited by 14 publications

(6 citation statements)

References 10 publications

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“…The method achieved 96.67% accuracy. Further implementation of different classifiers include SVM [20,21], k-means clustering [22,23] and fuzzy techniques [24][25][26]. The main challenge encountered by the methods based on traditional machine learning technique is due to the high dimension of the data.…”

Section: Introductionmentioning

confidence: 99%

A convolutional neural network–based learning approach to acute lymphoblastic leukaemia detection with automated feature extraction

Anwar

Alam

2020

Med Biol Eng Comput

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Leukaemia is a type of blood cancer which mainly occurs when bone marrow produces excess white blood cells in our body. This disease not only affects adult but also is a common cancer type among children. Treatment of leukaemia depends on its type and how far the disease has spread in the body. Leukaemia is classified into two types depending on how rapidly it grows: acute and chronic leukaemia. The early diagnosis of this disease is vital for effective treatment and recovery. This paper presents an automated diagnostic system to detect acute lymphoblastic leukaemia (ALL) using a convolutional neural network (CNN) model. The model uses labeled microscopic blood smear images to detect the malignant leukaemia cells. The current work uses data obtained from the Acute Lymphoblastic Leukaemia Image DataBase (ALL IDB) and performs various data augmentation techniques to increase the number of training data which in effect reduces the over-training problem. The model has been trained on 515 images using a fivefold validation technique achieving an accuracy of 95.54% and further tested on the remaining 221 images achieving almost 100% accuracy during most of the trials, maintaining an average of 99.5% accuracy. The method does not need any pre-processing or segmentation technique and works efficiently on raw data. This method can, hence, prove profitable for pathologist in diagnosing ALL efficiently.

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Section: Introductionmentioning

confidence: 99%

A convolutional neural network–based learning approach to acute lymphoblastic leukaemia detection with automated feature extraction

Anwar

Alam

2020

Med Biol Eng Comput

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“…The detection of abnormal cells is a key technique of the automatic screening system, and it is the prerequisite for the computer to automatically accurately find abnormal cells. Traditional methods first use image segmentation methods such as the watershed [4], k-means [5], etc., to segment the nuclei in images, then extract features such as optical density [6], texture [7], and morphology [8] of the nuclei, and finally use KNN [9], SVM [10] and other methods for classification. These methods adopts features designed artificially and can achieve good performance in general target detection tasks.…”

Section: Introductionmentioning

confidence: 99%

MACD R-CNN: An Abnormal Cell Nucleus Detection Method

Zhang

Cao

et al. 2020

IEEE Access

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The detection of abnormal cell nuclei is a key technique of the cytopathic automatic screening system, which directly determines the performance of the system. Although the Mask R-CNN which combines target detection and semantic segmentation has achieved good performance in general target detection tasks, the performance in abnormal cell detection is still unsatisfactory. To solve this problem, we design a new deep neural network for abnormal cell detection based on the Mask R-CNN, named mask abnormal cell detection R-CNN (MACD R-CNN). First, in the classification branch of Mask R-CNN, it generates the same size of feature maps from different size of RoIs as the input. The nuclei in this part of the feature maps will be deformed to varying degrees. We design a fixed proposal module to generate fixed-sized feature maps of nuclei, which allows the new information of nucleus is used for classification. Then we use the attention mechanism to merge the original RoI and Fixed RoI features. Finally, we increase the depth of the convolution layer to further improve the accuracy of cell classification. Experiments show that the MACD R-CNN can effectively improve the performance of abnormal cell detection.

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“…This manual process is however; tedious, slow, time-consuming, and is largely dependent on experienced experts in this field (Roussel et al, 2010[ 26 ]). Therefore, computer-aided methods that autonomously partially or fully perform some steps of this process, can be very useful and helpful for experts and researchers (Fatichah et al, 2012[ 7 ]).…”

Section: Introductionmentioning

confidence: 99%

Classification of chronic myeloid leukemia cell subtypes based on microscopic image analysis

Ghane

Vard

Talebi

et al. 2019

EXCLI Journal; 18:Doc382; ISSN 1611-2156

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Parameter Optimization of Local Fuzzy Patterns Based on Fuzzy Contrast Measure for White Blood Cell Texture Feature Extraction

Cited by 14 publications

References 10 publications

A convolutional neural network–based learning approach to acute lymphoblastic leukaemia detection with automated feature extraction

A convolutional neural network–based learning approach to acute lymphoblastic leukaemia detection with automated feature extraction

MACD R-CNN: An Abnormal Cell Nucleus Detection Method

Classification of chronic myeloid leukemia cell subtypes based on microscopic image analysis

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