MayGAN:Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

Leukemia,often called blood cancer,is a disease that primarily affects white blood cells(WBCs),which harms a person’s tissues and plasma.This condition may be fatal when if it is not diagnosed and recognized at an early stage.The physical technique and lab procedures for Leukaemia identification are considered time-consuming.It is crucial to use a quick and unexpected way to identify different forms of Leukaemia.Timely screening of the morphologies of immature cells is essential for reducing the severity of the disease and reducing the number of people who require treatment.Various deep-learning(DL)model-based segmentation and categorization techniques have already been introduced,although they still have certain drawbacks.In order to enhance feature extraction and classification in such a practical way,Mayfly optimization with Generative Adversarial Network(MayGAN)is introduced in this research.Furthermore,Generative Adversarial System(GAS)is integrated with Principal Component Analysis(PCA)in the feature-extracted model to classify the type of blood cancer in the data.The semantic technique and morphological procedures using geometric features are used to segment the cells that makeup Leukaemia.Acute lymphocytic Leukaemia(ALL),acute myelogenous Leukaemia(AML),chronic lymphocytic Leukaemia(CLL),chronic myelogenous Leukaemia(CML),and aberrant White Blood Cancers(WBCs)are all successfully classified by the proposed MayGAN model.The proposed MayGAN identifies the abnormal activity in the WBC,considering the geometric features.Compared with the state-of-the-art methods,the proposed MayGAN achieves 99.8%accuracy,98.5%precision,99.7%recall,97.4%F1-score,and 98.5%Dice similarity coefficient(DSC).

Image Smearing Modeling and Verification for Strapdown Star Sensor

To further extend study on celestial attitude determination with strapdown star sensor from static into dynamic field, one prerequisite is to generate precise dynamic simulating star maps. First a neat analytical solution of the smearing trajectory caused by spacecraft attitude maneuver is deduced successfully, whose parameters cover the geometric size of optics, three-axis angular velocities and CCD integral time. Then for the first time the mathematical law and method are discovered about how to synthesize the two formulae of smearing trajectory and the static Gaussian distribution function （GDF） model, the key of which is a line integral with regard to the static GDF attenuated by a factor 1/Ls （Ls is the arc length of the smearing trajectory） along the smearing trajectory. The dynamic smearing model is then obtained, also in an analytical form. After that, three sets of typical simulating maps and data are simulated from this dynamic model manifesting the expected smearing effects, also compatible with the linear model as its special case of no boresight rotation. Finally, model validity tests on a rate turntable are carried out, which results in a mean correlation coefficient 0.920 0 between the camera images and the corresponding model simulated ones with the same parameters. The sufficient similarity verifies the validity of the dynamic smearing model. This model, after pa- rameter calibration, can serve as a front-end loop of the ground semi-physical simulation system for celestial attitude determination with strapdown star sensor.

Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or tissues.Biomedical image processing concepts are identical to biomedical signal processing,which includes the investigation,improvement,and exhibition of images gathered using x-ray,ultrasound,MRI,etc.At the same time,cervical cancer becomes a major reason for increased women’s mortality rate.But cervical cancer is an identified at an earlier stage using regular pap smear images.In this aspect,this paper devises a new biomedical pap smear image classification using cascaded deep forest(BPSIC-CDF)model on Internet of Things(IoT)environment.The BPSIC-CDF technique enables the IoT devices for pap smear image acquisition.In addition,the pre-processing of pap smear images takes place using adaptive weighted mean filtering(AWMF)technique.Moreover,sailfish optimizer with Tsallis entropy(SFO-TE)approach has been implemented for the segmentation of pap smear images.Furthermore,a deep learning based Residual Network(ResNet50)method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear images.In order to showcase the improved diagnostic outcome of the BPSICCDF technique,a comprehensive set of simulations take place on Herlev database.The experimental results highlighted the betterment of the BPSICCDF technique over the recent state of art techniques interms of different performance measures.

Intelligent Deep Transfer Learning Based Malaria Parasite Detection andClassification Model Using Biomedical Image

Malaria is a severe disease caused by Plasmodium parasites,which can be detected through blood smear images.The early identification of the disease can effectively reduce the severity rate.Deep learning(DL)models can be widely employed to analyze biomedical images,thereby minimizing the misclassification rate.With this objective,this study developed an intelligent deep-transfer-learning-based malaria parasite detection and classification(IDTL-MPDC)model on blood smear images.The proposed IDTL-MPDC technique aims to effectively determine the presence of malarial parasites in blood smear images.In addition,the IDTL-MPDC technique derives median filtering(MF)as a pre-processing step.In addition,a residual neural network(Res2Net)model was employed for the extraction of feature vectors,and its hyperparameters were optimally adjusted using the differential evolution(DE)algorithm.The k-nearest neighbor(KNN)classifier was used to assign appropriate classes to the blood smear images.The optimal selection of Res2Net hyperparameters by the DE model helps achieve enhanced classification outcomes.A wide range of simulation analyses of the IDTL-MPDC technique are carried out using a benchmark dataset,and its performance seems to be highly accurate(95.86%),highly sensitive(95.82%),highly specific(95.98%),with a high F1 score(95.69%),and high precision(95.86%),and it has been proven to be better than the other existing methods.

Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

Cervical cancer is screened by pap smear methodology for detection and classification purposes.Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues.In this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes problem.Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells.Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)classifier.The success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%sensitivity.On top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high accuracy.On the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%accuracy.The proposed system is a built-in cascading manner with five models of polynomial(SVM)classifier.The overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model

Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases.An important kind of biomedical image is Pap smear image that iswidely employed for cervical cancer diagnosis.Cervical cancer is a vital reason for increased women’s mortality rate.Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer.Computer-aided systems for cancerous cell detection need to be developed using deep learning(DL)approaches.This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification(IDCNN-CDC)model using biomedical pap smear images.The proposed IDCNN-CDC model involves four major processes such as preprocessing,segmentation,feature extraction,and classification.Initially,the Gaussian filter(GF)technique is applied to enhance data through noise removal process in the Pap smear image.The Tsallis entropy technique with the dragonfly optimization(TE-DFO)algorithm determines the segmentation of an image to identify the diseased portions properly.The cell images are fed into the DL based SqueezeNet model to extract deeplearned features.Finally,the extracted features fromSqueezeNet are applied to the weighted extreme learning machine(ELM)classification model to detect and classify the cervix cells.For experimental validation,the Herlev database is employed.The database was developed at Herlev University Hospital(Denmark).The experimental outcomes make sure that higher performance of the proposed technique interms of sensitivity,specificity,accuracy,and F-Score.