Classification of acute lymphoblastic leukemia using improved ANFIS

Rejula, M. Anline; Amutha, S.; Shilpa, G. M.

doi:10.1007/s11042-023-15113-6

Cited by 3 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was proposed an improved Adaptive Network-Based Fuzzy Inference Systems (ANFIS) model to predict leukemia data using an Euclidean distance to measure between the trained feature data and the test feature data [33]. An Improved Adaptive Neuro-Fuzzy Neural Network (ANFNN) is also introduced, which helps the input space be partitioned into many local regions by the fuzzy Computer Sciences clustering, in which the computation complexity is decreased and, based on both the separation and the compactness among the clusters, the fuzzy rule number is determined by the validity function.…”

Section: Methodsmentioning

confidence: 99%

Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia

Curtidor,

Kussul,

Baydyk

et al. 2023

Eureka: PE

View full text Add to dashboard Cite

Analysis of white blood cells from blood can help to detect Acute Lymphoblastic Leukemia, a potentially fatal blood cancer if left untreated. The morphological analysis of blood cells images is typically performed manually by an expert; however, this method has numerous drawbacks, including slow analysis, low precision, and the results depend on the operator’s skill. We have developed and present here an automated method for the identification and classification of white blood cells using microscopic images of peripheral blood smears. Once the image has been obtained, we propose describing it using brightness, contrast, and micro-contour orientation histograms. Each of these descriptions provides a coding of the image, which in turn provides n parameters. The extracted characteristics are presented to an encoder’s input. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. This paper presents the performance of one classifier, the Random Threshold Classifier. The classifier’s output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. As shown below, the proposed neural Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned on 80 % training set and 20 % testing set for. Our system of image recognition is evaluated using the public dataset of peripheral blood samples from Acute Lymphoblastic Leukemia Image Database. It is important to mention that our system could be implemented as a computational tool for detection of other diseases, where blood cells undergo alterations, such as Covid-19

show abstract

Section: Methodsmentioning

confidence: 99%

Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia

Curtidor,

Kussul,

Baydyk

et al. 2023

Eureka: PE

View full text Add to dashboard Cite

show abstract

DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification

Saeed¹,

Kumar

Khuhro

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

Morphological diagnosis of hematologic malignancy using feature fusion-based deep convolutional neural network

Yadav,

Kumar,

Jalal

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Leukemia is a cancer of white blood cells characterized by immature lymphocytes. Due to blood cancer, many people die every year. Hence, the early detection of these blast cells is necessary for avoiding blood cancer. A novel deep convolutional neural network (CNN) 3SNet that has depth-wise convolution blocks to reduce the computation costs has been developed to aid the diagnosis of leukemia cells. The proposed method includes three inputs to the deep CNN model. These inputs are grayscale and their corresponding histogram of gradient (HOG) and local binary pattern (LBP) images. The HOG image finds the local shape, and the LBP image describes the leukaemia cell's texture pattern. The suggested model was trained and tested with images from the AML-Cytomorphology_LMU dataset. The mean average precision (MAP) for the cell with less than 100 images in the dataset was 84%, whereas for cells with more than 100 images in the dataset was 93.83%. In addition, the ROC curve area for these cells is more than 98%. This confirmed proposed model could be an adjunct tool to provide a second opinion to a doctor.

show abstract

Classification of acute lymphoblastic leukemia using improved ANFIS

Cited by 3 publications

References 26 publications

Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia

Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia

DeepLeukNet—A CNN based microscopy adaptation model for acute lymphoblastic leukemia classification

Morphological diagnosis of hematologic malignancy using feature fusion-based deep convolutional neural network

Contact Info

Product

Resources

About