A new endoscopic ultrasonography image processing method to evaluate the prognosis for pancreatic cancer treated with interstitial brachytherapy

AIM:To develop a fuzzy classification method to score the texture features of pancreatic cancer in endoscopic ultrasonography(EUS)images and evaluate its utility in making prognosis judgments for patients with unresectable pancreatic cancer treated by EUS-guided interstitial brachytherapy.METHODS:EUS images from our retrospective database were analyzed.The regions of interest were drawn,and texture features were extracted,selected,and scored with a fuzzy classification method using a C++program.Then,patients with unresectable pancreatic cancer were enrolled to receive EUS-guided iodine 125 radioactive seed implantation.Their fuzzy classification scores,tumor volumes,and carbohydrate antigen 199(CA199)levels before and after the brachytherapy were recorded.The association between the changes in these parameters and overall survival was analyzed statistically.RESULTS:EUS images of 153 patients with pancreatic cancer and 63 non-cancer patients were analyzed.A total of 25 consecutive patients were enrolled,and they tolerated the brachytherapy well without any complications.There was a correlation between the change in the fuzzy classification score and overall survival(Spearman test,r=0.616,P=0.001),whereas no correlation was found to be significant between the change in tumor volume(P=0.663),CA199 level(P=0.659),and overall survival.There were 15 patients with a decrease in their fuzzy classification score after brachytherapy,whereas the fuzzy classification score increased in another 10 patients.There was a significant difference in overall survival between the two groups(67 d vs 151 d,P=0.001),but not in the change of tumor volume and CA199 level.CONCLUSION:Using the fuzzy classification method to analyze EUS images of pancreatic cancer is feasible,and the method can be used to make prognosis judgments for patients with unresectable pancreatic cancer treated by interstitial brachytherapy.

EfficientNetB1 Deep Learning Model for Microscopic Lung Cancer Lesion Detection and Classification Using Histopathological Images

Cancer poses a significant threat due to its aggressive nature,potential for widespread metastasis,and inherent heterogeneity,which often leads to resistance to chemotherapy.Lung cancer ranks among the most prevalent forms of cancer worldwide,affecting individuals of all genders.Timely and accurate lung cancer detection is critical for improving cancer patients’treatment outcomes and survival rates.Screening examinations for lung cancer detection,however,frequently fall short of detecting small polyps and cancers.To address these limitations,computer-aided techniques for lung cancer detection prove to be invaluable resources for both healthcare practitioners and patients alike.This research implements an enhanced EfficientNetB1 deep learning model for accurate detection and classification using histopathological images.The proposed technique accurately classifies the histopathological images into three distinct classes:(1)no cancer(benign),(2)adenocarcinomas,and(3)squamous cell carcinomas.We evaluated the performance of the proposed technique using the histopathological(LC25000)lung dataset.The preprocessing steps,such as image resizing and augmentation,are followed by loading a pretrained model and applying transfer learning.The dataset is then split into training and validation sets,with fine-tuning and retraining performed on the training dataset.The model’s performance is evaluated on the validation dataset,and the results of lung cancer detection and classification into three classes are obtained.The study’s findings show that an enhanced model achieves exceptional classification accuracy of 99.8%.

Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in women.Early detection and effective treatment of BC can help save women’s lives.Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques.This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data set.The novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(DL),traditional machine learning(ML)techniques,and enhanced classification models have been deployed using the curated dataset.The analysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced LR(ELR)models for BC detection outperformed most of the existing models with impressive results.

Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases.Magnetic resonance imaging(MRI)is a widely utilized tool for the classification and detection of prostate cancer.Since the manual screening process of prostate cancer is difficult,automated diagnostic methods become essential.This study develops a novel Deep Learning based Prostate Cancer Classification(DTL-PSCC)model using MRI images.The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors.In addition,the fuzzy k-nearest neighbour(FKNN)model is utilized for classification process where the class labels are allotted to the input MRI images.Moreover,the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm(KHA)which results in improved classification performance.In order to demonstrate the good classification outcome of the DTL-PSCC technique,a wide range of simulations take place on benchmark MRI datasets.The extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%.

Horizontal Voting Ensemble Based Predictive Modeling System for Colon Cancer

Colon cancer is the third most commonly diagnosed cancer in the world.Most colon AdenoCArcinoma(ACA)arises from pre-existing benign polyps in the mucosa of the bowel.Thus,detecting benign at the earliest helps reduce the mortality rate.In this work,a Predictive Modeling System(PMS)is developed for the classification of colon cancer using the Horizontal Voting Ensemble(HVE)method.Identifying different patterns inmicroscopic images is essential to an effective classification system.A twelve-layer deep learning architecture has been developed to extract these patterns.The developedHVE algorithm can increase the system’s performance according to the combined models from the last epochs of the proposed architecture.Ten thousand(10000)microscopic images are taken to test the classification performance of the proposed PMS with the HVE method.The microscopic images obtained from the colon tissues are classified intoACAor benign by the proposed PMS.Results prove that the proposed PMS has∼8%performance improvement over the architecture without using the HVE method.The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2%of sensitivity and 99.4%of specificity.The overall accuracy of the proposed PMS is 99.3%,and without using the HVE method,it is only 91.3%.

A system for detection of cervical precancerous in field emission scanning electron microscope images using texture features

This study develops a novel cervical precancerous detection system by using texture analysis of field emission scanning electron microscopy(FE-SEM)images.The processing scheme adopted in the proposed system focused on two steps.The first step was to enhance cervical cell FE-SEM images in order to show the precancerous characterization indicator.A problem arises from the question of how to extract features which characterize cervical precancerous cells.For the first step,a preprocessing technique called intensity transformation and morphological operation(ITMO)algorithm used to enhance the quality of images was proposed.The algo-rithm consisted of contrast stretching and morphological opening operations.The second step was to characterize the cervical cells to three classes,namely normal,low grade intra-epithelial squamous lesion(LSIL),and high grade intra-epithelial squamous lesion(HSIL).To differen-tiate between normal and precancerous cells of the cervical cell FE-SEM images,human papillomavirus(HPV)contained in the surface of cells were used as indicators.In this paper,we investigated the use of texture as a tool in determining precancerous cell images based on the observation that cell images have a distinct visual texture.Gray level co-occurrences matrix(GLCM)technique was used to extract the texture features.To confirm the system's perfor-mance,the system was tested using 150 cervical cell FE-SEM images.The results showed that the accuracy,sensitivity and specificity of the proposed system are 95.7%,95.7%and 95.8%,respectively.

Pixel’s Quantum Image Enhancement Using Quantum Calculus

The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity values.The technique focuses on boosting the edges and texture of an image while leaving the smooth areas alone.The brain Magnetic Resonance Imaging(MRI)scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain cancer.Accurately detecting brain cancer is a complex challenge that the medical system faces when diagnosing the disease.To solve this issue,this research offers a quantum calculus-based MRI image enhancement as a pre-processing step for brain cancer diagnosis.The proposed image enhancement approach improves images with low gray level changes by estimating the pixel’s quantum probability.The suggested image enhancement technique is demonstrated to be robust and resistant to major quality changes on a variety ofMRIscan datasets of variable quality.ForMRI scans,the BRISQUE“blind/referenceless image spatial quality evaluator”and the NIQE“natural image quality evaluator”measures were 39.38 and 3.58,respectively.The proposed image enhancement model,according to the data,produces the best image quality ratings,and it may be able to aid medical experts in the diagnosis process.The experimental results were achieved using a publicly available collection of MRI scans.

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution Model

Lung cancer is the leading cause of mortality in the world affectingboth men and women equally.When a radiologist just focuses on the patient’sbody, it increases the amount of strain on the radiologist and the likelihoodof missing pathological information such as abnormalities are increased.One of the primary objectives of this research work is to develop computerassisteddiagnosis and detection of lung cancer. It also intends to make iteasier for radiologists to identify and diagnose lung cancer accurately. Theproposed strategy which was based on a unique image feature, took intoconsideration the spatial interaction of voxels that were next to one another.Using the U-NET+Three parameter logistic distribution-based technique, wewere able to replicate the situation. The proposed technique had an averageDice co-efficient (DSC) of 97.3%, a sensitivity of 96.5% and a specificity of94.1% when tested on the Luna-16 dataset. This research investigates howdiverse lung segmentation, juxta pleural nodule inclusion, and pulmonarynodule segmentation approaches may be applied to create Computer AidedDiagnosis (CAD) systems. When we compared our approach to four otherlung segmentation methods, we discovered that ours was the most successful.We employed 40 patients from Luna-16 datasets to evaluate this. In termsof DSC performance, the findings demonstrate that the suggested techniqueoutperforms the other strategies by a significant margin.

卷积神经网络在肝癌病理图像诊断中的应用综述

肝癌是一种恶性肝肿瘤,起源于肝细胞.肝癌诊断一直是医学难点问题,也是各领域研究的热点问题,早期确诊肝癌可以降低肝癌的死亡率.组织病理学图像检查是肿瘤学诊断的黄金标准,图像会显示组织切片的细胞和组织结构,可以用于确定细胞类型、组织结构、异常细胞的数量和形态,并评估肿瘤具体情况.本文重点研究了卷积神经网络针对病理图像的肝癌诊断算法,包括肝肿瘤检测、图像分割以及术前预测这3个方面的应用,详细阐述了卷积神经网络各算法的设计思路和相关改进目的及方法,以便为研究人员提供更清晰的参考思路.总结性分析了卷积神经网络算法在诊断中的优缺点,并对未来可能的研究热点和相关难点进行了探讨.

双气相定量CT对轻中度COPD的评估价值研究

目的:通过配准的双气相定量CT对轻中度慢性阻塞性肺疾病(COPD)患者肺气肿定量指标的对比分析,寻找最优肺气肿定量指标。方法:回顾性收集健康体检对照组55例和慢性阻塞性肺疾病全球倡议(GOLD)1级21例,GOLD 2级31例。将CT原始DICOM数据导入“数字肺”分析平台,测定深吸气末LAA%_(-950)和深呼气末LAA%_(-910)。将呼气相与吸气相CT图像配准,根据阈值法计算出肺气肿区域百分比(PRM~(Emph%))、功能性小气道病变区域百分比(PRMfSAD%)和正常区域百分比(PRM~(Normal%))。肺功能指标包括FVC、FEV_1%、FEV_1/FVC。组间一般资料、CT定量指标和肺功能差异采用独立样本t检验、Mann-Whitney U检验或卡方检验,相关性采用Spearman相关分析。绘制受试者工作特征(ROC)曲线分析CT定量参数对轻中度COPD患者肺气肿的诊断效能。结果:轻中度COPD患者与正常对照组间性别、吸烟指数、FEV1%、FEV1/FVC、吸气相LAA%_(-950)、呼气相LAA%_(-910)、PRM~(Emph%)、PRMfSAD%及PRMNormal%差异均有统计学意义。吸气相LAA%_(-950)与FEV1/FVC呈负相关,呼气相LAA%_(-910)、PRM~(Emph%)与FVC、FEV1%、FEV1/FVC均呈负相关。ROC曲线分析结果显示吸气相LAA%_(-950)、呼气相LAA%_(-910)及PRM~(Emph%)曲线下面积分别为0.742、0.861、0.876,其中PRM~(Emph%)指标的曲线下面积最大,对应临界值为9.84%,敏感度76.90%,特异度94.50%。结论:定量CT肺气肿指标吸气相LAA%_(-950)、呼气相LAA%_(-910)及双气相PRM~(Emph%)都能够客观评估轻中度COPD患者的肺气肿情况,其中PRM~(Emph%)是最优评估指标。

IN SITU IMAGING OF BREAST CANCER CELLS USING GREEN SEMICONDUCTOR QUANTUM DOTS

The breast cancer is the most common cause of cancer death in women. To establish an early stage in situ imaging of breast cancer cells, green quantum dots （QDs） are used as a fluorescent signal generator. The QDs based imaging of breast cancer cells involves anti-HER2/neu antibody for labeling the over expressed HER2 on the surface of breast cancer cells. The complete assay involves breast cancer cells, biotin labeled antibody and streptavidin conjugated QDs. The breast cancer cells are grown in culture plates and exposed to the biotin labeled antibodies, and then exposed to streptavidin labeled QDs to utilize the strong and stable biotin-streptavidin interaction. Fluorescent images of the complete assay for breast cancer cells are evaluated on a microscope with a UV light source. Results show that the breast cancer cells in the complete assay are used as fluorescent cells with brighter signals compared with those labeled by the organic dye using similar parameters and the same number of cells.

基于炎癌转化理论从气津分阶段论治恶性肺结节的研究进展

肺结节是局部的微小病灶,患者多无症状或仅有轻微不适。中医学认为肺结节虽长在局部,但是全身气血津液代谢逐步失衡所致,这与“炎癌转化”学说的理念一致。基于炎癌转化理论从气津分阶段论治恶性肺结节,转化前期以化痰祛湿为主,转化中期需加大活血消肿之药的占比,转化后期需酌加攻癌解毒之力,结合患者的体质、证候予以益气、理气、化气之法且各有侧重,以延缓甚至阻断肺结节恶化。

基于舌象特征逻辑回归的肺癌风险预警模型研究

目的分析良恶性肺结节的客观化舌诊数据特征,并基于逻辑回归方法建立肺癌风险预警模型。方法选取2020年7月-2022年3月上海中医药大学附属龙华医院肿瘤科263例肺癌患者(肺癌组),上海中医药大学附属曙光医院体检中心292例良性肺结节患者(良性肺结节组)和307例健康体检者(健康对照组),使用TFDA-1型数字舌面诊仪采集3组受试者的舌象图像,通过特征提取技术获取舌象客观诊断特征,分析3组受试者舌象指标分布特征,通过特征筛选后基于逻辑回归方法建立肺癌预警模型,并使用敏感性、特异性、准确率及受试者工作特征(ROC)曲线下面积(AUC)评估模型性能。结果良性肺结节组舌象特征与健康对照组相近;肺癌组与健康对照组、肺癌组与良性肺结节组舌象特征差异较大,肺癌患者舌象偏晦黯、舌质偏红、舌苔偏薄黄腻。基于舌象数据的肺癌预警模型准确率为70.09%、敏感性为69.94%、特异性为70.29%、AUC为0.769。在舌象数据集基础上加入基线信息后重新建模,模型诊断效能提升,基于基线信息与舌象数据的新模型准确率为77.30%、敏感性为75.94%、特异性为79.15%、AUC为0.812。结论良性肺结节患者与肺癌患者客观舌象数据统计特征存在显著差异,基于舌象客观数据的肺癌分类模型表现良好,中医客观舌诊数据可为良性肺结节和肺癌的鉴别诊断提供参考。

基于全局-局部注意力机制和YOLOv5的宫颈细胞图像异常检测模型

目标建立一种新的基于全局-局部注意机制和YOLOv5的宫颈病变细胞检测模型(Trans-YOLOv5),为准确、高效地分析宫颈细胞学图像并做出诊断提供帮助。方法使用共含有7410张宫颈细胞学图像且均包含对应真实标签的公开数据集。采用结合了数据扩增方式与标签平滑等技巧的YOLOv5网络结构实现对宫颈病变细胞的多分类检测。在YOLOv5骨干网络引用CBT3以增强深层全局信息提取能力,设计ADH检测头提高检测头解耦后定位分支对纹理特征的结合能力,从而实现全局-局部注意机制的融合。结果实验结果表明Trans-YOLOv5优于目前最先进的方法。mAP和AR分别达到65.9%和53.3%,消融实验结果验证了Trans-YOLOv5各组成部分的有效性。结论本文发挥不同注意力机制分别在全局特征与局部特征提取能力的差异,提升YOLOv5对宫颈细胞图像中异常细胞的检测精度,展现了其在自动化辅助宫颈癌筛查工作量的巨大潜力。

规范化流程管理在非小细胞肺癌患者PET-CT检查中的应用效果

目的:探讨规范化流程管理在非小细胞肺癌患者PET-CT检查中的应用效果,为确保临床非小细胞肺癌的诊疗质量提供参考依据。方法:选取2019年3月—2021年10月河南省肿瘤医院收治的112例非小细胞肺癌患者作为研究对象,按照随机数表法分组,患者均接受PET-CT检查,且检查期间,予以对照组(56例)患者原工作流程操作,予以观察组(56例)患者规范化流程管理,比较两组患者图像合格率、检查前准备完好率、检查时准备完好率、检测的等待时间和检查时间,干预前后焦虑、抑郁情绪评分,以及患者对护理工作的满意度。结果:观察组患者检查前准备完好率、检查时准备完好率高于对照组,差异有统计学意义(χ^(2)=5.617、1.734,P<0.05),同时观察组患者检测的等待时间和检查时间均较对照组缩短,差异有统计学意义(t=5.469、4.941,P<0.05)。与干预前比,干预后两组患者情绪焦虑自评量表(SAS)、抑郁自评量表(SDS)评分降低,且试验组患者低于对照组,差异有统计学意义(t=11.298、8.414,P<0.05)。观察组患者对护理工作的满意度高于对照组,差异有统计学意义(χ^(2)=4.350,P<0.05)。结论:非小细胞肺癌患者PET-CT检查中应用规范化流程管理可缩短患者的检查等待和检查时间,提升诊疗效率,并取得良好的图像质量,患者对护理工作的满意度高。

N6-甲基腺苷在疾病中的研究进展

N6-甲基腺苷(m6A)修饰是由甲基化酶和去甲基化酶调节,导致一个动态和可逆的过程。m6A水平的变化涉及广泛的细胞过程,包括核RNA输出、mRNA代谢、蛋白质翻译和RNA剪接,与各种疾病有很强的相关性。本文旨在归纳和总结mRNA中m6A表达水平的变化在常见三类疾病中的作用和机制,以及基于m6A在mRNA中水平变化作为药物干预靶点的研究趋势。

双源CT增强多参数成像在肺结节诊断中的临床价值

目的探讨双源CT增强多参数成像在肺结节诊断中的临床价值。方法回顾性分析本院2018年10月—2021年12月间接受双源CT增强多参数成像检查患者51例,根据病理活检结果分为良性组(良性肺结节21例)和恶性组(恶性肺结节30例)。将双源CT增强多参数成像图像传入Siemens双源CT专用Dual-Energy软件,通过勾画感兴趣区记录结节不同keV时单能量CT值。比较两组患者双源CT增强多参数数据,同时绘制ROC曲线计算各参数鉴别诊断良恶性肺结节的阈值。结果良性组患者的NIC、K绝对值、碘基值及常规增强CT值均明显高于恶性组(P<0.01);良性组患者静脉期40、70、100、120及140keV下的CT值均明显高于恶性组(P<0.01)。ROC曲线分析显示,NIC的AUC面积明显高于K绝对值和碘基值(P<0.05),NIC+K绝对值+碘基值联合诊断的AUC面积均明显高于单独诊断(P<0.05)。结论双源CT增强多参数成像可作为肺结节良恶性鉴别诊断的有效影像学手段,具有多参数、可量化等优势,对肺结节早期诊断和治疗指导均具有重要意义。

An Intelligent Algorithm for Skin Cancer Detection

Nowadays, computer vision as an interdisciplinary field is growing in different areas such as medical, electronics, etc. In the field, detection and particularly image segmentation is an essential task in which is difficult to find the appropriate one based on the application. In this paper, a new algorithm is proposed to segment the lesion from background. The algorithm is based on log edge detector with iterative median filtering. We have tested our algorithm on 20 dermoscopic images and compare the lesion detection results with those manually segmented by dermatologists. The experiments represent the effectiveness of proposed algorithm.

Computer-aided clinical image analysis as a predictor of sentinel lymph node positivity in cutaneous melanoma

BACKGROUND Delays in sentinel lymph node(SLN)biopsy may affect the positivity of non-SLNs.For these reasons,effort is being directed at obtaining reliable information regarding SLN positivity prior to surgical excision.However,the existing tools,e.g.,dermoscopy,do not recognize statistically significant predictive criteria for SLN positivity in melanomas.AIM To investigate the possible association of computer-assisted objectively obtained color,color texture,sharpness and geometry variables with SLN positivity.METHODS We retrospectively reviewed and analyzed the computerized medical records of all patients diagnosed with cutaneous melanoma in a tertiary hospital in Germany during a 3-year period.The study included patients with histologically confirmed melanomas with Breslow>0.75 mm who underwent lesion excision and SLN biopsy during the study period and who had clinical images shot with a digital camera and a handheld ruler aligned beside the lesion.RESULTS Ninety-nine patients with an equal number of lesions met the inclusion criteria and were included in the analysis.Overall mean(±standard deviation)age was 66(15)years.The study group consisted of 20 patients with tumor-positive SLN(SLN+)biopsy,who were compared to 79 patients with tumor-negative SLN biopsy specimen(control group).The two groups differed significantly in terms of age(61 years vs 68 years)and histological subtype,with the SLN+patients being younger and presenting more often with nodular or secondary nodular tumors(P<0.05).The study group patients showed significantly higher eccentricity(i.e.distance between color and geometrical midpoint)as well as higher sharpness(i.e.these lesions were more discrete from the surrounding normal skin,P<0.05).Regarding color variables,SLN+patients demonstrated higher range in all four color intensities(gray,red,green,blue)and significantly higher skewness in three color intensities(gray,red,blue),P<0.05.Color texture variables,i.e.lacunarity,were comparable in both groups.CONCLUSION SLN+patients demonstrated significantly higher eccentricity,higher sharpness,higher range in all four color intensities(gray,red,green,blue)and significantly higher skewness in three color intensities(gray,red,blue).Further prospective studies are needed to better understand the effectiveness of clinical image processing in SLN+melanoma patients.

Cognitive Computing-Based Mammographic Image Classification on an Internet of Medical

Recently,the Internet of Medical Things(IoMT)has become a research hotspot due to its various applicability in medical field.However,the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment,generating a massive quantity of healthcare data.In such cases,cognitive computing can be employed that uses many intelligent technologies-machine learning(ML),deep learning(DL),artificial intelligence(AI),natural language processing(NLP)and others-to comprehend data expansively.Furthermore,breast cancer(BC)has been found to be a major cause of mortality among ladies globally.Earlier detection and classification of BC using digital mammograms can decrease the mortality rate.This paper presents a novel deep learning-enabled multi-objective mayfly optimization algorithm(DLMOMFO)for BC diagnosis and classification in the IoMT environment.The goal of this paper is to integrate deep learning(DL)and cognitive computing-based techniques for e-healthcare applications as a part of IoMT technology to detect and classify BC.The proposed DL-MOMFO algorithm involved Adaptive Weighted Mean Filter(AWMF)-based noise removal and contrast-limited adaptive histogram equalisation(CLAHE)-based contrast improvement techniques to improve the quality of the digital mammograms.In addition,a U-Net architecture-based segmentation method was utilised to detect diseased regions in the mammograms.Moreover,a SqueezeNet-based feature extraction and a fuzzy support vector machine(FSVM)classifier were used in the presented technique.To enhance the diagnostic performance of the presented method,the MOMFO algorithm was used to effectively tune the parameters of the SqueezeNet and FSVM techniques.The DL-MOMFO technique was tested on the MIAS database,and the experimental outcomes revealed that the DL-MOMFO technique outperformed existing techniques.