Automatic Leukaemia Segmentation Approach for Blood Cancer Classification Using Microscopic Images

Leukaemia is a type of blood cancer that is caused by undeveloped White Blood Cells(WBC),and it is also called a blast blood cell.In the marrow of human bones,leukaemia is developed and is responsible for blood cell generation with leukocytes and WBC,and if any cell gets blasted,then it may become a cause of death.Therefore,the diagnosis of leukaemia in its early stages helps greatly in the treatment along with saving human lives.Subsequently,in terms of detection,image segmentation techniques play a vital role,and they turn out to be the important image processing steps for the extraction of feature patterns from the Acute Lymphoblastic Leukaemia(ALL)type of blood cancer.Moreover,the image segmentation technique focuses on the division of cells by segmenting a microscopic image into background and cancer blood cell nucleus,which is well-known as the Region Of Interest(ROI).As a result,in this article,we attempt to build a segmentation technique capable of solving blood cell nucleus segmentation issues using four distinct scenarios,including K-means,FCM(Fuzzy Cmeans),K-means with FFA(Firefly Algorithm),and FCM with FFA.Also,we determine the most effective method of blood cell nucleus segmentation,which we subsequently use for the Leukaemia classification model.Finally,using the Convolution Neural Network(CNN)as a classifier,we developed a leukaemia cancer classification model from the microscopic images.The proposed system’s classification accuracy is tested using the CNN to test the model on the ALL-IDB dataset and equate it to the current state of the art.In terms of experimental analysis,we observed that the accuracy of the model is near to 99%,and it is far better than other existing models that are designed to segment and classify the types of leukaemia cancer in terms of ALL.

Multi-omics:Powerful accelerator for uncovering plant specialized metabolic pathways:The case of leonurine

As sessile organisms,plants employ a unique adaptive strategy to survive harsh terrestrial environments and defend against or interact with coevolving animals and microorganisms.This strategy involves expanding their metabolic systems,resulting in the production of plant specialized metabolites(PSMs)or natural products.Chemically,PSMs constitute a highly diverse group of compounds based on their common core structure,comprising terpenoids,phenylpropanoids,benzenoids,alkaloids and nitrogen-containing compounds,glucosinolates,indoles and sulfur-containing indole compounds,phenolics,and fatty acid derivatives(Garagounis et al.,2021).

Investigation of Nanoscale Scratching on Copper with Conical Tools Using Particle-Based Simulation

In this study,a modeling approach based on smooth particle hydrodynamics(SPH)was implemented to simulate the nanoscale scratching process using conical tools with different negative rake angles.The implemented model enables the study of the topography of groove profiles,scratching forces,and the residual plastic strain beneath the groove.An elastoplastic material model was employed for the workpiece,and the tool–workpiece interaction was defined by a contact model adopted from the Hertz theory.An in-house Lagrangian SPH code was implemented to perform nano-scratching simulations.The SPH simulation results were compared with nanoscale scratching experimental data available in the literature.The simulation results revealed that the normal force was more dominant compared to the cutting force,in agreement with experimental results reported for a conical tip tool with a 60°negative rake angle.In addition,the simulated groove profile was in good agreement with the groove profile produced in the aforementioned experiment.The numerical simulations also showed that the normal and cutting forces increased with the increase in the scratching depth and rake angle.Although the cutting and ploughing mechanisms were noticed in nano-scratching,the ploughing mechanism was more dominant for increased negative rake angles.It was also observed that residual plastic strain exists below the groove surface,and that the plastically deformed layer thickness beneath a scratched groove is larger for more negative values of the tool rake angle and higher scratching depths.