Blood Cell Images Research Articles

A Novel Deep Learning Approach to Malaria Disease Detection on Two Malaria Datasets

Malaria is a contagious febrile disease transmitted to humans by the bite of female mosquitoes. It is important to diagnose this disease in a short period of time. Finding the mathematically best numerical solution to a particular problem is the most important issue for most departments. In deep learning-based systems developed, the difference between the real data and the predicted result of the model is measured using loss functions. To minimize the error rate in the predictions during the training process of deep learning models, the weight values used in the model should be updated. This update process has a significant effect on the model prediction result. This article presents a new deep learning-based malaria detection method that will help diagnose malaria in a short time. A new 21-layer Convolutional Neural Network (CNN) model is designed and proposed to describe infected and uninfected thin red blood cell images. By using thin red blood cell sample images, 95% accuracy was achieved with Nadam and RMSprop optimization techniques. The results obtained show the efficiency of the proposed method according to each optimization algorithm.

Retracted: A Method for Expanding the Training Set of White Blood Cell Images

Retracted: A Method for Expanding the Training Set of White Blood Cell Images

No neuroprotective effect of therapeutic hypothermia following lipopolysaccharide-sensitized hypoxia-ischemia: a newborn piglet study

IntroductionTherapeutic hypothermia is the only proven neuroprotective treatment for hypoxic-ischemic encephalopathy. However, studies have questioned whether therapeutic hypothermia may benefit newborns subjected to infection or inflammation before a hypoxic-ischemic insult. We aimed to compare newborn piglets with lipopolysaccharide-sensitized hypoxia-ischemia treated with and without therapeutic hypothermia with regards to measures of neuroprotection.MethodsA total of 32 male and female piglets were included in this randomized experimental study. Lipopolysaccharides from Escherichia coli were infused intravenously before initiation of a standardized global hypoxic-ischemic insult. The piglets were then randomized to either normothermia or therapeutic hypothermia. After 14 h, the piglets were evaluated. Our primary outcome was brain lactate/N-acetylaspartate ratio assessed by magnetic resonance spectroscopy. Secondary outcomes included measures of magnetic resonance imaging, amplitude-integrated electroencephalography, immunohistochemistry, and concentration of blood cells and cytokines.ResultsPiglets treated with and without therapeutic hypothermia were subjected to comparable global hypoxic-ischemic insults. We found no difference between the two groups with regards to measures of magnetic resonance spectroscopy and imaging, amplitude-integrated electroencephalography, immunohistochemistry, and concentration of blood cells and cytokines.ConclusionWe found no indication of neuroprotection by therapeutic hypothermia in newborn piglets following lipopolysaccharide-sensitized hypoxia-ischemia. However, interpretation of the results is limited by the short observation period. Further studies are required to determine the potential clinical implications of these findings.

Optofluidic-based cell multi-axis controllable rotation and 3D surface imaging

The controlled rotation of individual cells plays a crucial role in enabling three-dimensional multi-angle observation of cellular structure, function, and dynamic processes. Reported cell rotation techniques often struggle to strike a balance between high precision and simple control, and they exhibit limited control flexibility, typically achieving only uniaxial cell rotation. In this study, we propose a cell rotation technique in three dimensions based on optofluidics, which utilizes optical tweezers to immobilize the cell and exploits the asymmetry of the surrounding flow to drive cell rotation. By adjusting the focal position of the optical tweezers, cells can be positioned within various flow profiles, enabling control of the rotation speed, rotation direction, and rotation axis of cells. This approach simplifies the manipulation procedure, achieving desirable control precision and greater rotation flexibility. Using our approach, multi-angle surface imaging projections of target cells can be rapidly obtained, followed by capturing the cell contour data from the images. By combining the cell contour data with corresponding angular position information, we have reconstructed the 3D surface of the target cell. We have employed this technique in experiments for the analysis of red blood cell morphology. Based on the constructed 3D surface images of diverse-shaped red blood cells, we quantified structural parameters including cell surface area, volume, sphericity, and surface roughness, which demonstrates the potential application of this cell rotation technique for cellular morphological analysis.

Automatic Recognition of Blood Cell Images with Dense Distributions Based on a Faster Region-Based Convolutional Neural Network

In modern clinical medicine, the important information of red blood cells, such as shape and number, is applied to detect blood diseases. However, the automatic recognition problem of single cells and adherent cells always exists in a densely distributed medical scene, which is difficult to solve for both the traditional detection algorithms with lower recognition rates and the conventional networks with weaker feature extraction capabilities. In this paper, an automatic recognition method of adherent blood cells with dense distribution is proposed. Based on the Faster R-CNN, the balanced feature pyramid structure, deformable convolution network, and efficient pyramid split attention mechanism are adopted to automatically recognize the blood cells under the conditions of dense distribution, extrusion deformation, adhesion and overlap. In addition, the Align algorithm for region of interest also contributes to improving the accuracy of recognition results. The experimental results show that the mean average precision of cell detection is 0.895, which is 24.5% higher than that of the original network model. Compared with the one-stage mainstream networks, the presented network has a stronger feature extraction capability. The proposed method is suitable for identifying single cells and adherent cells with dense distribution in the actual medical scene.

Classification of microscopic peripheral blood cell images using multibranch lightweight CNN-based model

Classification of microscopic peripheral blood cell images using multibranch lightweight CNN-based model

Implementation towards Blood Cancer Detection with Convolutional Neural Network

This Blood cancer is a life-threatening disease that requires early and accurate detection for effective treatment. In this project, we present a novel approach for blood cancer detection using a Convolutional Neural Network (CNN) model. The CNN model is trained on a dataset comprising cancer and normal blood cell images. Through extensive analysis and evaluation, we achieve a high level of accuracy in distinguishing between cancerous and normal blood cells. To evaluate the performance of our model, we conducted tests on a separate set of cancer and normal blood cell images. The accuracy of our model was determined by comparing the predicted labels with the ground truth labels. The results demonstrate that our CNN model achieves a commendable accuracy rate, making it a promising tool for blood cancer detection. Furthermore, we discuss the significance of our findings and their potential implications for early diagnosis and improved treatment outcomes. The robustness and reliability of our model contribute to its practical utility in clinical settings. By enabling early detection of blood cancer, our approach has the potential to positively impact patient outcomes and enhance the efficiency of treatment strategies. In conclusion, this project presents a novel approach for blood cancer detection using a CNN model. The results demonstrate the effectiveness of our model in accurately distinguishing between cancerous and normal blood cells. The proposed method holds promise for improving blood cancer diagnosis and ultimately contributing to better patient care.

A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes that carry the infection. This disease has significant and devastating consequences on the health systems of fragile countries, particularly in sub-Saharan Africa. Malaria affects red blood cells by invading and replicating within them, destroying them, and releasing toxic byproducts into the bloodstream. The parasite’s ability to stick and modify the surface of red blood cells can cause them to become sticky, obstructing blood flow in vital organs such as the brain and spleen. Therefore, efficient approaches for the early detection of malaria are critical to saving patients’ lives. The main aim of this study is to develop an efficient model for early malaria diagnosis. We used malaria images based on parasitized and uninfected red blood cells for the study experiments. We applied neural network-based approaches such as Neural Search Architecture Network (NASNet) and compared its performance with machine learning techniques. Moreover, we proposed a novel NNR (NASNet Random forest) method for feature engineering. The proposed NNR approach first extracts spatial features from input malaria images, then class prediction probability features are extracted from these spatial features. The feature set obtained from the data extraction trains machine learning models. Our comprehensive experiments show that the support vector machine outperformed state-of-the-art models, achieving a high-performance score of 99% and having an inference time near 0.025 s. We validated the performance using k-fold cross-validation and optimized the hyperparameters through tuning. Our proposed research has improved the early diagnosis of malaria and can assist medical specialists in reducing the mortality rate.

A combined neural network mechanism for categorizing the normal and cancer cells

The dangerous form of acute lymphocytic leukemia damages the bone marrow tissue and white blood cells. Unformed white blood cells multiply and exchange healthy cells in the bone marrow. Everything spreads fast and, if not noticed, can be dangerous in some months. As a result, computer assisted diagnosis of acute lymphocytic leukemia has the possible to protect several lives, but it needs a high-precision categorization of malignant cells, that is difficult due to the graphical similarity of malignant and normal cells. Though deep convolutional neural networks and the classic machine learning algorithms have exposed excellent results in classifying blood cell images, they have not been able to make effective use of the long-term correlation between some important image attributes and image labeling. This study introduced Long short-term memory (LSTM) to solve this difficulty. This study actually combines VGG16 neural networks and LSTM, which provide the VGG16-LSTM framework, which improves the recognizing of image content and learns the structural features of images, as well as initiate big data training in clinical image processing. The transfer learning approach was used to transfer pre-trained weight parameters to the VGG16 area in the ImageNet database, and the custom loss function was used to train and integrate the network quickly and accurately with the weight parameters. Experimental findings reveal that the suggested network approach is more relevant and effective in categorizing cancer cell images than other existing methods.

Low-Cost Automated Microscopy and Morphology-Based Machine Learning Classification of Sickle Cell Disease and Beta-Thalassemia in Nepal and Canada

INTRODUCTION Sickle cell disease (SCD) is the most common inherited blood disorder, and is associated with a high mortality rate for children in low-resource settings, where the disease burden is high. Early screening and treatment options reduce morbidity and mortality. In addition to screening for SCD, screening for asymptomatic, heterozygous conditions, such as sickle cell trait (SCT) and β-thalassemia trait, is required for effective SCD and thalassemia screening programs. However, rural and remote communities lack accurate low-cost screening techniques. The goal of this study is to augment a common low-cost screening test, the sickling test, using automated microscopy and machine learning to classify individuals with SCD (HbS/β-thalassemia and HbSS), trait conditions (HbAS and HbA/β-thalassemia), and those without common beta-globin disorders (HbAA) for implementation in Nepal. As HbC mutation is not encountered in Nepal, screening for HbSC or HbAC was not included. Additionally, we aim to evaluate the feasibility of 5 low-cost techniques to supplement/replace the local gold standard test (Hb HPLC), which is inaccessible in rural/remote settings. METHODS In this study (ClinicalTrials.gov Identifier: NCT05506358), blood samples were collected and tested at respective clinical sites in Nepal (Mount Sagarmatha Polyclinic and Diagnostic Center, Nepalgunj) and Canada (St. Paul's Hospital and BC Children's Hospital, Vancouver). Participants (138 total: 111 Nepal; 27 Canada) between ages 2 to 74 whose diagnoses were previously established by Hb HPLC were recruited. Five low-cost tests (our augmented sickling test, HbS solubility test, HemoTypeSC, Sickle SCAN, Gazelle Hb variant test) and Hb HPLC were performed on all participants (30 HbAA; 23 HbA/β-thalassemia; 45 HbAS; 11 HbS/β-thalassemia; 29 HbSS). Informed consent was provided by the participants or parents, according to protocols approved by institutional/national research ethics boards. For the sickling test, a sealed wet preparation of blood mixed with 2% sodium metabisulphite was imaged with a robotic microscope (Octopi) with automated scanning/focusing. The images of blood cells were segmented using Cellpose 2.0, allowing for morphological characterization of cells and morphology based classification, with an 80:20 participant-wise split of training and testing data. RESULTS Using high-throughput imaging, more than 300,000 de-identified images of blood films were collected, resulting in more than 1.5 trillion segmented cells. The de-identified images and processed data will be released in an open-access data repository. The frequency distribution of 11 different morphological parameters were used for machine learning based classification, resulting in testing sensitivity and specificity of 82.9% and 82.4%, respectively. The confusion matrix (Figure 1) shows that sensitivity of detecting SCD cases (HbS/β-thalassemia and HbSS) was 98.6%, and most misclassifications were between trait and normal conditions. Comparing the different low-cost techniques (Table 1), the Gazelle Hb variant test had the highest sensitivity and specificity (97.0% and 99.3%), but is more than twice as expensive than the augmented sickling test, while lateral flow assays (HemoTypeSC and SickleSCAN) had lower sensitivity (74-75%) than the augmented sickling test, due to their inability to detect β-thalassemia trait. CONCLUSIONS The sickling test, which is traditionally unable to distinguish between SCT and SCD, was augmented to detect SCD and trait conditions (including β-thalassemia) with an overall sensitivity of 82.4% (98.6% sensitivity for SCD detection). The preliminary results indicate that image-based classification can be a promising low-cost and automated screening tool for low-resource settings, reducing the need for highly-trained personnel for device operation and disease detection. Furthermore, the open-access image dataset can be used to improve classification in the future, as segmentation/classification algorithms improve over time.

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning

White blood cells (WBCs) are an indispensable constituent of the immune system. Efficient and accurate categorization of WBC is a critical task for disease diagnosis by medical experts. This categorization helps in the correct identification of medical problems. In this research work, WBC classes are categorized with the help of a transform learning model in combination with our proposed virtual hexagonal trellis (VHT) structure feature extraction method. The VHT feature extractor is a kernel-based filter model designed over a square lattice. In the first step, Graft Net CNN model is used to extract features of augmented data set images. Later, the VHT base feature extractor extracts useful features. The CNN-extracted features are passed to ant colony optimization (ACO) module for optimal features acquisition. Extracted features from the VHT base filter and ACO are serially merged to create a single feature vector. The merged features are passed to the support vector machine (SVM) variants for optimal classification. Our strategy yields 99.9% accuracy, which outperforms other existing methods.

Leukemia Blood Cells Detection using Neural Network Classifier

Image segmentation is an image processing operation performed on the image in order to partition the image into some images based on the information contained in the original image. Image segmentation plays an important role in many medical imaging applications, image segmentation facilitates the anatomy process in a particular body of human body. Classification and clustering are the methods used un data mining for analyzing the data sets and divide them on the basis of some particular classification rules. There are many image segmentation tools that used for medical purpose, so it is necessary to define and/or to improve the image segmentation methods in order to get the best method. In this study, the image of leukemia and red blood cells will be used as samples to determine the best algorithm in image segmentation. The procedure for doing segmentation itself is clustering image, edge detection on image, and image classification. The clustering is to extract important information from an image. The edge detection is to determine the existence of edges of lines in image in order to investigate and localize the desired edge features. Moreover, the classification analyzes the properties of some images and organizes the information into certain categories. In this study, the Neural Network and K-Nearest Neighbor are used for image classification by paired with Local Binary Pattern and Principal Component Analysis. The results revealed that the best method of proven in classifying images is from Local Binary Pattern feature extraction with the average accuracy of 94%.

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

A-143 Imaging Flow Cytometry: A Novel Method Using ADVIA 2120i System Reagents and Microscopic Image Analysis

Abstract Background Flow cytometry based ADVIA 2120i Hematology System reagents are used to modify blood cells. Laser-scattering flow cytometry replaced with digital microscopic image analysis significantly simplifies the complex system to a compact, low-cost microscope. A novel method for imaging flow cytometry is proposed here that simultaneously generates complete blood count (CBC) and high-resolution images of blood cells. Methods Image processing algorithms are implemented to process the pixels containing blood cells. RBCs are made isovolumetrically spherical using the RBC reagent (Fig. 1A) and imaged. RBC volumes are easily determined from the diameter of the isovolumetrically spherical RBCs. By illuminating the spherical RBCs with approximately400 nm light, the increasing grayscale values of the pixels corresponds to increasing haemoglobin concentration due to absorption. WBC nuclei are stained using the Perox reagent (Fig. 1B). The grayscale values of the nuclei pixels can be used to determine the peroxidase stain component, thereby obtaining cell size-to-peroxidase activity scatter plots similar to those on the ADVIA 2120i system. Using the Baso reagent, the cytoplasm of all WBCs are stripped off except for basophils, as shown in Fig. 1C. All the microscopic images have good contrast and high resolution. Image processing techniques can thus provide a 5-part differential using the nuclei/lobular density of WBCs. Results Images were obtained under a 20×x Nikon microscope with a Thorlabs DCC1545M monochrome camera. ADVIA reagents were mixed with venous blood and <5 µL of reagent treated sample was spotted between a microscopic slide and coverslip. Image analysis was performed using ImageJ particle analysis tools. Conclusion Analysis of digital microscopic images of blood cells acted upon by ADVIA 2120i system reagents can offer a novel, simplified, cost-effective method of hematology analysis. Simultaneous CBC scatter plots and high-resolution images of the blood cells can be obtained using this new approach.Fig. 1.(a) Isovolumetricallly spherical RBCs, (b) peroxidase stain of WBC nuclei, and (c) cytoplasm of WBCs stripped off (except for basophils). Images taken under a 20× Nikon microscope. *Under development and not available for commercial sale.

Performance of the new MC-80 automated digital cell morphology analyser in detection of normal and abnormal blood cells: Comparison with the CellaVision DM9600.

Mindray MC-80 is an automated system for digital imaging of white blood cells (WBCs) and their pre-classification. The objective of this work is to analyse its performance comparing it with the CellaVision® DM9600. A total of 445 samples were used, 194 normal and 251 abnormal: acute leukaemia (100), myelodysplastic syndromes/myeloproliferative neoplasms (33), lymphoid neoplasms (50), plasma cell neoplasms (14), infections (49) and thrombocytopenia (5). WBC pre-classification values with the MC-80 and DM9600 were compared with (1) the microscope, (2) Mindray BC-6800Plus differentials in only normal samples, and (3) confirmed or reclassified images (post-classification). Pearson's correlation, Lin's concordance, Passing-Bablok regression, and Bland-Altman plots were used. Sensitivity, specificity, positive (PPV) and negative (NPV) predictive values for abnormal cells using the MC-80 were calculated. The PPV and NPV were above 98% and 99%, for normal samples. For immature granulocytes (IG), NPV and PPV were 100% and 74.2%. When comparing the WBC differentials using the MC-80, the microscope and the BC-6800Plus, no differences were found except for basophils and IG. Our results showed good agreement between the pre- and post-classification of normal WBC, including IG, quantified by high correlation and concordance values (0.91-1). Sensitivity and specificity for blasts were 0.984 and 0.640. The MC-80 detected abnormal lymphocytes in 30% of the smears from patients with lymphoid neoplasm. Plasma cell identification was better using the DM9600. The sensitivity and specificity for erythroblast detection were 1 and 0.890. We found that the MC-80 shows high performance for WBC differentials for both normal samples and patients with haematological diseases.

Implementation of Hybrid CNN-XGBoost Method for Leukemia Detection Problem

Leukemia is a blood cancer in which blood cells become malignant and uncontrolled. It can cause damage to the function of the body's organs. Several machine learning methods have been used to automatically detect biomedical images, including blood cell images. In this study, we utilized a hybrid machine learning method, called a hybrid Convolutional Neural Network-eXtreme Gradient Boosting (CNN-XGBoost) method to detect leukemia in blood cells. The hybrid method combines two machine learning methods. We use CNN as the basic classifier and XGBoost as the main classification method. The aim of this methodology was to assess whether incorporating the basic classification method would lead to an enhancement in the performance of the main classification model. The experimental findings demonstrated that the utilization of XGBoost as the main classifier led to a marginal increase in accuracy, elevating it from 85.32% to 85.43% compared to the basic CNN classification. This research highlights the potential of hybrid machine learning approaches in biomedical image analysis and their role in advancing the early diagnosis of leukemia and potentially other medical conditions.

Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification.

The examination, counting, and classification of white blood cells (WBCs), also known as leukocytes, are essential processes in the diagnosis of many disorders, including leukemia, a kind of blood cancer characterized by the uncontrolled proliferation of carcinogenic leukocytes in the marrow of the bone. Blood smears can be chemically or microscopically studied to better understand hematological diseases and blood disorders. Detecting, identifying, and categorizing the many blood cell types are essential for disease diagnosis and therapy planning. A theoretical and practical issue. However, methods based on deep learning (DL) have greatly helped blood cell classification. Images of blood cells in a microscopic smear were collected from GitHub, a public source that uses the MIT license. An end-to-end computer-aided diagnosis (CAD) system for leukocytes has been created and implemented as part of this study. The introduced system comprises image preprocessing and enhancement, image segmentation, feature extraction and selection, and WBC classification. By combining the DenseNet-161 and the cyclical learning rate (CLR), we contribute an approach that speeds up hyperparameter optimization. We also offer the one-cycle technique to rapidly optimize all hyperparameters of DL models to boost training performance. The dataset has been split into two sets: approximately 80% of the data (9,966 images) for the training set and 20% (2,487 images) for the validation set. The validation set has 623, 620, 620, and 624 eosinophil, lymphocyte, monocyte, and neutrophil images, whereas the training set has 2,497, 2,483, 2,487, and 2,499, respectively. The suggested method has 100% accuracy on the training set of images and 99.8% accuracy on the testing set. Using a combination of the recently developed pretrained convolutional neural network (CNN), DenseNet, and the one fit cycle policy, this study describes a technique of training for the classification of WBCs for leukemia detection. The proposed method is more accurate compared to the state of the art.

Image preprocessing and hyperparameter optimization on pretrained model MobileNetV2 in white blood cell image classification

White blood cells play a role in maintaining the immune system which consists of several types such as neutrophils, lymphocytes, monocytes, eosinophils and basophils. MobileNetV2 is one of the pretrained convolutional neural network (CNN) models that provides excellent advantages and performance in classifying images. In this research was conducted to find out how to apply optimization hyperparameters and the impact of image processing on white blood cell image classification using MobileNetV2, so that it is expected to find a combination of preprocessing and combination of hyperparameter values that can produce the highest accuracy value. To maximize the classification process, before classifying the image, several stages of image preprocessing are carried out, namely cropping, grayscale, resizing and augmentation. Hyperparameter tuning was carried out for an experiment to improve model performance. The three main parameters used in hyperparameter tuning are learning rate, batch size, and number of epochs. Performance optimization model performance will be measured using accuracy, sensitivity, specificity and using a confusion matrix. Based on the experimental results in this study, it shows that the best learning rate value is 0.00001, the best batch size value is 32, and the best epoch value is 250.

Correlation of Sysmex-XN9000 and CellaVision Advanced RBC software on anisocytosis

ABSTRACTObjectiveThis study aims to evaluate the consistency of heterogeneity degree of erythrocyte volume parameters between the blood automated analyzer Sysmex-XN9000 and the advanced red blood cell software CellaVisionDI-60.Method500 blood samples of volunteers were analyzed by Sysmex-XN9000 and CellaVision-DI60. The sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, and false negative rate were evaluated. The consistency of all parameters was tested.ResultTaking the standard RBC group as the control group, the RBC parameters of the macrocytic and the microcytic group were compared. There was a statistical difference between the groups. ROC curve analysis showed that the best cutoff value of microcytic and of macrocytic affecting MCV were 4.1% and 5.7%, respectively. The best cutoff value of anisocytosis was 15.0%. The correlation coefficient between anisocytosis and red blood cell distribution width (RDW-CV) was 0.756. The sensitivity, specificity, positive predictive value and coincidence rate of anisocytosis were high. The false negative rate was 10.0%, and the false positive rate was 7.4%.ConclusionAll parameters of the degree of heterogeneity have good accuracy and consistency in the two instruments. Anisocytosis is with higher coincidence rate and positive predictive value. MIC and MAC have a good prediction on the increase or decrease of MCV. Although advanced RBC software's false negative and false positive rates are high, the red blood cell image system is more intuitive and time-saving in observing cells. Consequently, CellaVision-DI60 is suggested to combine with XN-9000 for judging the anisocytosis in daily work comprehensively.

Classification of Acute Lymphocytic Leukemic Blood Cell Images using Hybrid CNN-Enhanced Ensemble SVM Models and Machine Learning Classifiers

Acute Lymphocytic Leukemia is a dangerous kind of malignant cancer caused due to the overproduction of white blood cells. The white blood cells in our body are responsible for fighting against infections, if the WBC increases the immunity will decrease and it would lead to serious health conditions. Malignant cancers such as ALL is life threatening if the disease is not diagnosed at an early stage. If a person is suffering from ALL the disease needs to be diagnosed at an early stage before it starts spreading, if it starts spreading the person’s chances of survival would also reduce. Here comes the need of an accurate automated system which would assist the oncologists to diagnose the disease as early as possible. In this paper some of the algorithms that are enhanced to detect and classify ALL are incorporated. In order to classify the Acute Lymphocytic Leukemia a hybrid model has been deployed to improve the accuracy of the diagnosis and it is termed as Hybrid CNN Enhanced Ensemble SVM for the classification of malignancy. Machine Learning classifiers are also used to design the system and it is then compared with enhanced CNN based on the performance metrics.