Analysis of an electronic methods for nasopharyngeal carcinoma: Prevalence, diagnosis, challenges and technologies

Modeling and optimal analysis of lung cancer cell growth and apoptosis with fractional-order dynamics

This study explores the application of fractional-order calculus in modeling lung cancer cell growth dynamics, emphasizing its advantages over traditional integer-order models. Conventional models often fail to capture the complexities of tumor behavior, such as memory effects and long-range interactions. The fractional-order logistic equation provides a more sophisticated framework that integrates intrinsic growth rates and environmental constraints, enabling a nuanced analysis of tumor progression and treatment responses. A key component of this research involves deriving a Laplace domain representation to assess transfer function characteristics, which aids in evaluating stability and response across various frequency domains. An improved fractional-order model was developed to illustrate the interplay between cancer proliferation and immune response mechanisms. The optimization of critical parameters, including the fractional-order ultimate growth rate, has been achieved using a genetic algorithm (GA) optimization. The main findings of this work include the potential of fractional-order modeling to understand, analyze, and determine treatment strategies, ultimately advancing the understanding of cancer dynamics and improving patient outcomes in oncology. Here, it shows the application of fractional-order dynamics to determine the effective treatment procedure concerning all complex parameters involved. This research contributes to the growing body of knowledge on sophisticated mathematical frameworks in cancer research, facilitating the development of tailored therapeutic interventions based on individual patient profiles.

Value of radiomics and deep learning feature fusion models based on dce-mri in distinguishing sinonasal squamous cell carcinoma from lymphoma

Problem

Sinonasal squamous cell carcinoma (SNSCC) and sinonasal lymphoma (SNL) lack distinct clinical manifestations and traditional imaging characteristics, complicating the accurate differentiation between these tumors and the selection of appropriate treatment strategies. Consequently, there is an urgent need for a method that can precisely distinguish between these tumors preoperatively to formulate suitable treatment plans for patients.

Methods

This study aims to construct and validate ML and DL feature models based on Dynamic Contrast-Enhanced (DCE) imaging and to evaluate the clinical value of a radiomics and deep learning (DL) feature fusion model in differentiating between SNSCC and SNL. This study performed a retrospective analysis on the preoperative axial DCE-T1WI MRI images of 90 patients diagnosed with sinonasal tumors, comprising 50 cases of SNSCC and 40 cases of SNL. Data were randomly divided into a training set and a validation set at a 7:3 ratio, and radiomic features were extracted. Concurrently, deep learning features were derived using the optimally pre-trained DL model and integrated with manually extracted radiomic features. Feature sets were selected through independent samples t-test, Mann-Whitney U-test, Pearson correlation coefficient and LASSO regression. Three conventional machine learning (CML) models and three DL models were established, and all radiomic and DL features were merged to create three pre-fusion machine learning models (DLR). Additionally, a post-fusion model (DLRN) was constructed by combining radiomic scores and DL scores. Quantitative metrics such as area under the curve (AUC), sensitivity, and accuracy were employed to identify the optimal feature set and classifier. Furthermore, a deep learning-radiomics nomogram (DLRN) was developed as a clinical decision-support tool.

Results

The feature fusion model of radiomics and DL has higher accuracy in distinguishing SNSCC from SNL than CML or DL alone. The ExtraTrees model based on DLR fusion features of DCE-T1WI had an AUC value of 0.995 in the training set and 0.939 in the validation set.The DLRN model based on the fusion of predictive scores had an AUC value of 0.995 in the training set and 0.911 in the validation set.The DLRN model based on the fusion of predictive scores had an AUC value of 0.995 in the training set and 0.911 in the validation set.

Conclusion

This study, by constructing a feature integration model combining radiomics and deep learning (DL), has demonstrated strong predictive capabilities in the preoperative non-invasive diagnosis of SNSCC and SNL, offering valuable information for tailoring personalized treatment plans for patients.

A clinical-radiomics nomogram based on multisequence MRI for predicting the outcome of patients with advanced nasopharyngeal carcinoma receiving chemoradiotherapy

Problem

Nasopharyngeal carcinoma (NPC) is a common malignant tumor with high heterogeneity and is mainly treated with chemoradiotherapy. It is important to predict the outcome of patients with advanced NPC after chemoradiotherapy to devise customized treatment strategies. Traditional MRI methods have limited predictive power, and better predictive models are needed.

Aim

To evaluate the predictive value of a clinical-radiomics nomogram based on multisequence MRI in predicting the outcome of advanced NPC patients receiving chemoradiotherapy.

Methods

This prospective study included a retrospective analysis of 118 patients with advanced NPC who underwent MRI prior to chemoradiotherapy. The primary endpoint was progression-free survival (PFS). The maximum ROIs of lesions at the same level were determined via axial T2-weighted imaging short-time inversion recovery (T2WI-STIR), contrast-enhanced T1-weighted imaging (CE-T1WI), and diffusion-weighted imaging (DWI) with solid tumor components, and the radiomic features were extracted. After feature selection, the radiomics score was calculated, and a nomogram was constructed combining the radiomics score with the clinical features. The diagnostic efficacy of the model was evaluated by the area under the receiver operating characteristic curve (AUC), and the clinical application value of the nomogram was evaluated by decision curve analysis (DCA) and a correction curve. Patients were divided into a high-risk group and a low-risk group, and the median risk score calculated by the joint prediction model was used as the cutoff value. Kaplan-Meier analysis and the log-rank test were used to compare the differences in survival curves between the two groups.

Results

The AUCs of the nomogram model constructed by the combination of the radiomics score and neutrophil-to-lymphocyte ratio (NLR) and T stage in the training group and validation group were 0.897 (95% CI: 0.825-0.968) and 0.801 (95% CI: 0.673-0.929), respectively. Kaplan-Meier survival analysis demonstrated that the model effectively stratified patients into high- and low-risk groups, with significant differences in prognosis.

Conclusion

This clinical-radiomics nomogram based on multisequence MRI offers a noninvasive, effective tool for predicting the outcome of advanced NPC patients receiving chemoradiotherapy, promoting individualized treatment approaches.

Uncommon Presentations of Nasopharyngeal Carcinoma: A Report of Two Cases

Nasopharyngeal carcinoma (NPC), a rare form of squamous cell carcinoma originating from the nasopharynx epithelium, exhibits a higher prevalence in southern China, Southeast Asia, the Arctic, North Africa, and the Middle East, with significant incidence in northeastern India, particularly Nagaland. Commonly presenting with nasal and otological symptoms, NPC diagnosis is challenging due to its diverse clinical manifestations. This case report highlights two atypical NPC cases: a 32-year-old female presenting with chronic headache and giddiness and a 22-year-old male with severe right-sided facial pain and trismus. Both cases underwent extensive diagnostic procedures, including imaging and biopsies, ultimately confirming NPC. Treatment involved radiotherapy and chemotherapy, resulting in significant symptom improvement. These cases underscore the importance of recognizing unusual NPC presentations to facilitate early diagnosis and treatment, improving patient outcomes.

MFNet: Meta-learning based on frequency-space mix for MRI segmentation in nasopharyngeal carcinoma

Deep learning techniques have been applied to medical image segmentation and demonstrated expert-level performance. Due to the poor generalization abilities of the models in the deployment in different centres, common solutions, such as transfer learning and domain adaptation techniques, have been proposed to mitigate this issue. However, these solutions necessitate retraining the models with target domain data and annotations, which limits their deployment in clinical settings in unseen domains. We evaluated the performance of domain generalization methods on the task of MRI segmentation of nasopharyngeal carcinoma (NPC) by collecting a new dataset of 321 patients with manually annotated MRIs from two hospitals. We transformed the modalities of MRI, including T1WI, T2WI and CE-T1WI, from the spatial domain to the frequency domain using Fourier transform. To address the bottleneck of domain generalization in MRI segmentation of NPC, we propose a meta-learning approach based on frequency domain feature mixing. We evaluated the performance of MFNet against existing techniques for generalizing NPC segmentation in terms of Dice and MIoU. Our method evidently outperforms the baseline in handling the generalization of NPC segmentation. The MF-Net clearly demonstrates its effectiveness for generalizing NPC MRI segmentation to unseen domains (Dice = 67.59%, MIoU = 75.74% T1W1). MFNet enhances the model's generalization capabilities by incorporating mixed-feature meta-learning. Our approach offers a novel perspective to tackle the domain generalization problem in the field of medical imaging by effectively exploiting the unique characteristics of medical images.

Machine learning models for predicting the onset of chronic kidney disease after surgery in patients with renal cell carcinoma

BACKGROUND

Patients with renal cell carcinoma (RCC) have an elevated risk of chronic kidney disease (CKD) following nephrectomy. Therefore, continuous monitoring and subsequent interventions are necessary. It is recommended to evaluate renal function postoperatively. Therefore, a tool to predict CKD onset is essential for postoperative follow-up and management.

METHODS

We constructed a cohort using data from eight tertiary hospitals from the Korean Renal Cell Carcinoma (KORCC) database. A dataset of 4389 patients with RCC was constructed for analysis from the collected data. Nine machine learning (ML) models were used to classify the occurrence and nonoccurrence of CKD after surgery. The final model was selected based on the area under the receiver operating characteristic (AUROC), and the importance of the variables constituting the model was confirmed using the shapley additive explanation (SHAP) value and Kaplan-Meier survival analyses.

RESULTS

The gradient boost algorithm was the most effective among the various ML models tested. The gradient boost model demonstrated superior performance with an AUROC of 0.826. The SHAP value confirmed that preoperative eGFR, albumin level, and tumor size had a significant impact on the occurrence of CKD after surgery.

CONCLUSIONS

We developed a model to predict CKD onset after surgery in patients with RCC. This predictive model is a quantitative approach to evaluate post-surgical CKD risk in patients with RCC, facilitating improved prognosis through personalized postoperative care.

Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract

PROBLEM

Artificial intelligence has been widely investigated for diagnosis and treatment strategy design, with some models proposed for detecting oral pharyngeal, nasopharyngeal, or laryngeal carcinoma. However, no comprehensive model has been established for these regions.

AIM

Our hypothesis was that a common pattern in the cancerous appearance of these regions could be recognized and integrated into a single model, thus improving the efficacy of deep learning models.

METHODS

We utilized a point-wise spatial attention network model to perform semantic segmentation in these regions.

RESULTS

Our study demonstrated an excellent outcome, with an average mIoU of 86.3%, and an average pixel accuracy of 96.3%.

CONCLUSION

The research confirmed that the mucosa of oral pharyngeal, nasopharyngeal, and laryngeal regions may share a common appearance, including the appearance of tumors, which can be recognized by a single artificial intelligence model. Therefore, a deep learning model could be constructed to effectively recognize these tumors.

Deep Convolutional Neural Network for Nasopharyngeal Carcinoma Discrimination on MRI by Comparison of Hierarchical and Simple Layered Convolutional Neural Networks

Nasopharyngeal carcinoma (NPC) is one of the most common head and neck cancers. Early diagnosis plays a critical role in the treatment of NPC. To aid diagnosis, deep learning methods can provide interpretable clues for identifying NPC from magnetic resonance images (MRI). To identify the optimal models, we compared the discrimination performance of hierarchical and simple layered convolutional neural networks (CNN). Retrospectively, we collected the MRI images of patients and manually built the tailored NPC image dataset. We examined the performance of the representative CNN models including shallow CNN, ResNet50, ResNet101, and EfficientNet-B7. By fine-tuning, shallow CNN, ResNet50, ResNet101, and EfficientNet-B7 achieved the precision of 72.2%, 94.4%, 92.6%, and 88.4%, displaying the superiority of deep hierarchical neural networks. Among the examined models, ResNet50 with pre-trained weights demonstrated the best classification performance over other types of CNN with accuracy, precision, and an F1-score of 0.93, 0.94, and 0.93, respectively. The fine-tuned ResNet50 achieved the highest prediction performance and can be used as a potential tool for aiding the diagnosis of NPC tumors.

Machine learning-based prediction model for late recurrence after surgery in patients with renal cell carcinoma

Background

Renal cell carcinoma is characterized by a late recurrence that occurs 5 years after surgery; hence, continuous monitoring and follow-up is necessary. Prognosis of late recurrence of renal cell carcinoma can only be improved if it is detected early and treated appropriately. Therefore, tools for rapid and accurate renal cell carcinoma prediction are essential.

Methods

This study aimed to develop a prediction model for late recurrence after surgery in patients with renal cell carcinoma that can be used as a clinical decision support system for the early detection of late recurrence. We used the KOrean Renal Cell Carcinoma database that contains large-scale cohort data of patients with renal cell carcinoma in Korea. From the collected data, we constructed a dataset of 2956 patients for the analysis. Late recurrence and non-recurrence were classified by applying eight machine learning models, and model performance was evaluated using the area under the receiver operating characteristic curve.

Results

Of the eight models, the AdaBoost model showed the highest performance. The developed algorithm showed a sensitivity of 0.673, specificity of 0.807, accuracy of 0.799, area under the receiver operating characteristic curve of 0.740, and F1-score of 0.609.

Conclusions

To the best of our knowledge, we developed the first algorithm to predict the probability of a late recurrence 5 years after surgery. This algorithm may be used by clinicians to identify patients at high risk of late recurrence that require long-term follow-up and to establish patient-specific treatment strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01964-w.

Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches

Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. In lieu of that, this research employed machine learning approaches to develop models for identifying and visualizing relevant prognostic indications of breast cancer survival rates. A comprehensive hospital-based breast cancer dataset was collected from the National Cancer Institute's SEER Program's November 2017 update, which offers population-based cancer statistics. The dataset included female patients diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3). The dataset included nine predictor factors and one predictor variable that were linked to the patients' survival status (alive or dead). To identify important prognostic markers associated with breast cancer survival rates, prediction models were constructed using K-nearest neighbor (K-NN), decision tree (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression (LR), voting classifier, and support vector machine (SVM). All methods yielded close results in terms of model accuracy and calibration measures, with the lowest achieved from logistic regression (accuracy = 80.57 percent) and the greatest acquired from the random forest (accuracy = 94.64 percent). Notably, the multiple machine learning algorithms utilized in this research achieved high accuracy, suggesting that these approaches might be used as alternative prognostic tools in breast cancer survival studies, especially in the Asian area.

Comparative Analysis of Different Efficient Machine Learning Methods for Fetal Health Classification

Obstetricians often utilize cardiotocography (CTG) to assess a child's physical health throughout pregnancy because it gives data on the fetal heartbeat and uterine contractions, which helps identify whether the fetus is pathologic or not. Obstetricians have traditionally analyzed CTG data artificially, which takes time and is unreliable. As a result, creating a fetal health classification model is essential, as it may save not only time but also medical resources in the diagnosis process. Machine learning (ML) is currently extensively used in fields such as biology and medicine to address a variety of issues, due to its fast advancement. This research covers the findings and analyses of multiple machine learning models for fetal health classification. The method was developed using the open-access cardiotocography dataset. Although the dataset is modest, it contains some noteworthy values. The data was examined and used in a variety of ML models. For classification, random forest (RF), logistic regression, decision tree (DT), support vector classifier, voting classifier, and K-nearest neighbor were utilized. When the results are compared, it is discovered that the random forest model produces the best results. It achieves 97.51% accuracy, which is better than the previous method reported.

Machine Learning Based Comparative Analysis for Breast Cancer Prediction

One of the most prevalent and leading causes of cancer in women is breast cancer. It has now become a frequent health problem, and its prevalence has recently increased. The easiest approach to dealing with breast cancer findings is to recognize them early on. Early detection of breast cancer is facilitated by computer-aided detection and diagnosis (CAD) technologies, which can help people live longer lives. The major goal of this work is to take advantage of recent developments in CAD systems and related methodologies. In 2011, the United States reported that one out of every eight women was diagnosed with cancer. Breast cancer originates as a result of aberrant cell division in the breast, which leads to either benign or malignant cancer formation. As a result, early detection of breast cancer is critical, and with effective treatment, many lives can be saved. This research covers the findings and analyses of multiple machine learning models for identifying breast cancer. The Wisconsin Breast Cancer Diagnostic (WBCD) dataset was used to develop the method. Despite its small size, the dataset provides some interesting data. The information was analyzed and put to use in a number of machine learning models. For prediction, random forest, logistic regression, decision tree, and K-nearest neighbor were utilized. When the results are compared, the logistic regression model is found to offer the best results. Logistic regression achieves 98% accuracy, which is better than the previous method reported.

MRF-RFS: A Modified Random Forest Recursive Feature Selection Algorithm for Nasopharyngeal Carcinoma Segmentation

BACKGROUND

An accurate and reproducible method to delineate tumor margins is of great importance in clinical diagnosis and treatment. In nasopharyngeal carcinoma (NPC), due to limitations such as high variability, low contrast, and discontinuous boundaries in presenting soft tissues, tumor margin can be extremely difficult to identify in magnetic resonance imaging (MRI), increasing the challenge of NPC segmentation task.

OBJECTIVES

The purpose of this work is to develop a semiautomatic algorithm for NPC image segmentation with minimal human intervention, while it is also capable of delineating tumor margins with high accuracy and reproducibility.

METHODS

In this paper, we propose a novel feature selection algorithm for the identification of the margin of NPC image, named as modified random forest recursive feature selection (MRF-RFS). Specifically, to obtain a more discriminative feature subset for segmentation, a modified recursive feature selection method is applied to the original handcrafted feature set. Moreover, we combine the proposed feature selection method with the classical random forest (RF) in the training stage to take full advantage of its intrinsic property (i.e., feature importance measure).

RESULTS

To evaluate the segmentation performance, we verify our method on the T1-weighted MRI images of 18 NPC patients. The experimental results demonstrate that the proposed MRF-RFS method outperforms the baseline methods and deep learning methods on the task of segmenting NPC images.

CONCLUSION

The proposed method could be effective in NPC diagnosis and useful for guiding radiation therapy.

Deep learning for risk prediction in patients with nasopharyngeal carcinoma using multi-parametric MRIs

BACKGROUND

Magnetic resonance images (MRI) is the main diagnostic tool for risk stratification and treatment decision in nasopharyngeal carcinoma (NPC). However, the holistic feature information of multi-parametric MRIs has not been fully exploited by clinicians to accurately evaluate patients.

OBJECTIVE

To help clinicians fully utilize the missed information to regroup patients, we built an end-to-end deep learning model to extract feature information from multi-parametric MRIs for predicting and stratifying the risk scores of NPC patients.

METHODS

In this paper, we proposed an end-to-end multi-modality deep survival network (MDSN) to precisely predict the risk of disease progression of NPC patients. Extending from 3D dense net, this proposed MDSN extracted deep representation from multi-parametric MRIs (T1w, T2w, and T1c). Moreover, deep features and clinical stages were integrated through MDSN to more accurately predict the overall risk score (ORS) of individual NPC patient.

RESULT

A total of 1,417 individuals treated between January 2012 and December 2014 were included for training and validating the end-to-end MDSN. Results were then tested in a retrospective cohort of 429 patients included in the same institution. The C-index of the proposed method with or without clinical stages was 0.672 and 0.651 on the test set, respectively, which was higher than the that of the stage grouping (0.610).

CONCLUSIONS

The C-index of the model which integrated clinical stages with deep features is 0.062 higher than that of stage grouping alone (0.672 vs 0.610). We conclude that features extracted from multi-parametric MRIs based on MDSN can well assist the clinical stages in regrouping patients.

Nomogram Predicting the Benefits of Adding Concurrent Chemotherapy to Intensity-Modulated Radiotherapy After Induction Chemotherapy in Stages II-IVb Nasopharyngeal Carcinoma

BACKGROUND

To compare the efficacy of induction chemotherapy plus concurrent chemoradiotherapy (IC+CCRT) versus induction chemotherapy plus radiotherapy (IC+RT) in patients with locoregionally advanced nasopharyngeal carcinoma (NPC).

PATIENTS AND METHODS

One thousand three hundred twenty four patients with newly-diagnosed NPC treated with IC+CCRT or IC+RT were enrolled. Progression-free survival (PFS), distant metastasis-free survival (DMFS), overall survival (OS), locoregional relapse-free survival (LRFS), and acute toxicities during radiotherapy were compared using propensity score matching (PSM). A nomogram was developed to predict the 3- and 5-year PFS with or without concurrent chemotherapy (CC).

RESULTS

PSM assigned 387 patients to the IC+CCRT group and IC+RT group, respectively. After 3 years, no significant difference in PFS (84.7 vs. 87.5%, P = 0.080), OS (95.5 vs. 97.6%, P = 0.123), DMFS (89.7 vs. 92.8%, P = 0.134), or LRFS (94.0 vs. 94.1%, P = 0.557) was noted between the groups. Subgroup analysis indicated comparable survival outcomes in low-risk NPC patients (II-III with EBV DNA <4,000 copies/ml) between the groups, although IC+RT alone was associated with fewer acute toxicities. However, IC+CCRT was associated with significantly higher 3-year PFS, OS, DMFS, and LRFS rates, relative to IC+RT alone, in high-risk NPC patients (IVa-b or EBV DNA ≥4,000 copies/ml). Multivariate analysis showed that T category, N category, EBV DNA level, and treatment group were predictive of PFS, and were hence incorporated into the nomogram. The nomogram predicted that the magnitude of benefit from CC could vary significantly.

CONCLUSIONS

IC+RT had similar efficacy as IC+CCRT in low-risk NPC patients, but was associated with fewer acute toxicities. However, in high-risk patients, IC+CCRT was superior to IC+RT.

Computer-Aided Pathologic Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning

The pathologic diagnosis of nasopharyngeal carcinoma (NPC) by different pathologists is often inefficient and inconsistent. We have therefore introduced a deep learning algorithm into this process and compared the performance of the model with that of three pathologists with different levels of experience to demonstrate its clinical value. In this retrospective study, a total of 1970 whole slide images of 731 cases were collected and divided into training, validation, and testing sets. Inception-v3, which is a state-of-the-art convolutional neural network, was trained to classify images into three categories: chronic nasopharyngeal inflammation, lymphoid hyperplasia, and NPC. The mean area under the curve (AUC) of the deep learning model is 0.936 based on the testing set, and its AUCs for the three image categories are 0.905, 0.972, and 0.930, respectively. In the comparison with the three pathologists, the model outperforms the junior and intermediate pathologists, and has only a slightly lower performance than the senior pathologist when considered in terms of accuracy, specificity, sensitivity, AUC, and consistency. To our knowledge, this is the first study about the application of deep learning to NPC pathologic diagnosis. In clinical practice, the deep learning model can potentially assist pathologists by providing a second opinion on their NPC diagnoses.

Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images

Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size and the difficulty towards determining location of Blood Leucocytes. Physical analysis of blood tests to recognize leukocytes is tedious, time-consuming and liable to error because of the various morphological components of the cells. Segmentation of medical imagery has been considered as a difficult task because of complexity of images, and also due to the non-availability of leucocytes models which entirely captures the probable shapes in each structures and also incorporate cell overlapping, the expansive variety of the blood cells concerning their shape and size, various elements influencing the outer appearance of the blood leucocytes, and low Static Microscope Image disparity from extra issues outcoming about because of noise. We suggest a strategy towards segmentation of blood leucocytes using static microscope images which is a resultant of three prevailing systems of computer vision fiction: enhancing the image, Support vector machine for segmenting the image, and filtering out non ROI (region of interest) on the basis of Local binary patterns and texture features. Every one of these strategies are modified for blood leucocytes division issue, in this manner the subsequent techniques are very vigorous when compared with its individual segments. Eventually, we assess framework based by compare the outcome and manual division. The findings outcome from this study have shown a new approach that automatically segments the blood leucocytes and identify it from a static microscope images. Initially, the method uses a trainable segmentation procedure and trained support vector machine classifier to accurately identify the position of the ROI. After that, filtering out non ROI have proposed based on histogram analysis to avoid the non ROI and chose the right object. Finally, identify the blood leucocytes type using the texture feature. The performance of the foreseen approach has been tried in appearing differently in relation to the system against manual examination by a gynaecologist utilizing diverse scales. A total of 100 microscope images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual segmentation method for accurately determining the ROI. We have evaluated the blood leucocytes identification using the ROI texture (LBP Feature). The identification accuracy in the technique used is about 95.3%., with 100 sensitivity and 91.66% specificity.