Artificial intelligence for characterization of diminutive colorectal polyps:A feasibility study comparing two computer-aided diagnosis systems

BACKGROUND Artificial intelligence(AI)has potential in the optical diagnosis of colorectal polyps.AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system(CADx)AI for ColoRectal Polyps(AI4CRP)for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYE^(TM)(Fujifilm,Tokyo,Japan).CADx influence on the optical diagnosis of an expert endoscopist was also investigated.METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm.Both CADxsystems exploit convolutional neural networks.Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard.AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value(range 0.0-1.0).A predefined cut-off value of 0.6 was set with values<0.6 indicating benign and values≥0.6 indicating premalignant colorectal polyps.Low confidence characterizations were defined as values 40%around the cut-off value of 0.6(<0.36 and>0.76).Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps.Self-critical AI4CRP,excluding 14 low confidence characterizations[27.5%(14/51)],had a diagnostic accuracy of 89.2%,sensitivity of 89.7%,and specificity of 87.5%,which was higher compared to AI4CRP.CAD EYE had a 83.7%diagnostic accuracy,74.2%sensitivity,and 100.0%specificity.Diagnostic performances of the endoscopist alone(before AI)increased nonsignificantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE(AI-assisted endoscopist).Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems,except for specificity for which CAD EYE performed best.CONCLUSION Real-time use of AI4CRP was feasible.Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.

RepBoTNet-CESA:An Alzheimer’s Disease Computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global features.In contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel dimensions.However,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is disregarded.To tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features simultaneously.It combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification performance.Moreover,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer structure.Finally,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC task.This demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic tasks.Furthermore,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet performance.Besides,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.

Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis

The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computeraided diagnosis(CAD) vs human judgment alone in characterizing solitary pulmonary nodules(SPNs) at computed tomography(CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator(BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic(ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions(P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs(15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses(mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.

Computer-aided texture analysis combined with experts' knowledge: Improving endoscopic celiac disease diagnosis

AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease(CD).METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computerbased classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique(MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts visually classified each image as normal mucosa(Marsh-0) or villous atrophy(Marsh-3). The experts' decisions were further integrated into state-of-the-arttexture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts' diagnoses in 27 different settings.RESULTS: Compared to the experts' diagnoses, in 24 of 27 classification settings(consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant(P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95%(P < 0.001).CONCLUSION: The overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.

Computer-aided diagnosis for contrast-enhanced ultrasound in the liver

Computer-aided diagnosis(CAD) has become one of the major research subjects in medical imaging and diagnostic radiology.The basic concept of CAD is to provide computer output as a second opinion to assist radiologists' image interpretations by improving the accuracy and consistency of radiologic diagnosis and also by reducing the image-reading time.To date,research on CAD in ultrasound(US)-based diagnosis has been carried out mostly for breast lesions and has been limited in the fields of gastroenterology and hepatology,with most studies being conducted using B-mode US images.Two CAD schemes with contrast-enhanced US(CEUS) that are used in classifying focal liver lesions(FLLs) as liver metastasis,hemangioma,or three histologically differentiated types of hepatocellular carcinoma(HCC) are introduced in this article:one is based on physicians' subjective pattern classifications(subjective analysis) and the other is a computerized scheme for classification of FLLs(quantitative analysis).Classification accuracies for FLLs for each CAD scheme were 84.8% and 88.5% for metastasis,93.3% and 93.8% for hemangioma,and 98.6% and 86.9% for all HCCs,respectively.In addition,the classification accuracies for histologic differentiation of HCCs were 65.2% and 79.2% for well-differentiated HCCs,41.7% and 50.0% for moderately differentiated HCCs,and 80.0% and 77.8% for poorly differentiated HCCs,respectively.There are a number of issues concerning the clinical application of CAD for CEUS,however,it is likely that CAD for CEUS of the liver will make great progress in the future.

Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses

Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the variation in sonographic features between multiple observations and determine the effect of features variation on computer-aided diagnosis of the breast masses. Materials and Methods: Ultrasound images of biopsy proven solid breast masses were analyzed in three independent observations for BI-RADS sonographic features. The BI-RADS features from each observation were used with Bayes classifier to determine probability of malignancy. The observer agreement in the sonographic features was measured by kappa coefficient and the difference in the diagnostic performances between observations was determined by the area under the ROC curve, Az, and interclass correlation coefficient. Results: While some features were repeatedly observed, κ = 0.95, other showed a significant variation, κ = 0.16. For all features, combined intra-observer agreement was substantial, κ = 0.77. The agreement, however, decreased steadily to 0.66 and 0.56 as time between the observations increased from 1 to 2 and 3 months, respectively. Despite the variation in features between observations the probabilities of malignancy estimates from Bayes classifier were robust and consistently yielded same level of diagnostic performance, Az was 0.772-0.817 for sonographic features alone and 0.828-0.849 for sonographic features and age combined. The difference in the performance, ΔAz, between the observations for the two groups was small (0.003-0.044) and was not statistically significant (p < 0.05). Interclass correlation coefficient for the observations was 0.822 (CI: 0.787-0.853) for BI-RADS sonographic features alone and for those combined with age was 0.833 (CI: 0.800-0.862). Conclusion: Despite the differences in the BI-RADS sonographic features between different observations, the diagnostic performance of computer-aided analysis for differentiating breast masses did not change. Through continual retraining, the computer-aided analysis provides consistent diagnostic performance independent of the variations in the observed sonographic features.

Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland.Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care.This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques.Sequential forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this study.In ensemble learning,random forest,adaptive boosting,and bagging classifiers are employed.The effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature reduction.Extensive experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,respectively.Beyond enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical applications.It significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed scheme.This research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

基于Bayes时空模型分析HIV/AIDS晚发现的时空分布特征及其影响因素

【目的】旨在分析兰州市HIV/AIDS晚发现的时空聚集性特征及相关影响因素,明确兰州市HIV/AIDS晚发现高风险地区和时间趋势,为兰州市因地制宜地制定HIV/AIDS防治策略措施提供参考依据。【方法】选择兰州市2011-2018年间新报告的成年HIV/AIDS病例作为研究对象,研究中所需的数据资料来自兰州市疾病预防控制中心和兰州市统计年鉴。采用Bayes时空模型分析HIV/AIDS晚发现相对风险(RR)的时空分布特征及其影响因素。【结果】2011-2018年间兰州市新报告的HIV/AIDS病例共计1984例,其中HIV/AIDS晚发现者有982例(49.5%),平均年龄为39.67岁,男性占90.9%。老年人和女性HIV/AIDS病例中晚发现的比例更高;城关区(51.1%)、安宁区(50.3%)和榆中县(51.9%)具有高于平均水平的HIV/AIDS晚发现比例;2011-2018年间兰州市总体的晚发现比例呈波动上升趋势。Bayes时空模型分析结果显示,兰州市HIV/AIDS晚发现风险在2011-2015年间波动变化,而在2015年后迅速上升,其RR(95%CI)从1.01(0.84,1.23)上升到1.11(0.77,1.97);红古区和三个县的晚发现风险变化趋势与兰州市的总体变化趋势相似,而城关区和七里河区的晚发现风险呈下降趋势;晚发现相对风险大于1的区县包括:永登县(RR=1.07,95%CI:0.55,1.96)、西固区(RR=1.04,95%CI:0.67,1.49)、城关区(RR=2.41,95%CI:0.85,6.16)和七里河区(RR=2.03,95%CI:1.10,3.27)。冷热点分析结果显示城关区和七里河区为热点区。影响因素分析结果显示,随着人均GDP(RR=0.65,95%CI:0.35,0.90)和HIV/AIDS病例中的男性比例(RR=0.53,95%CI:0.19,0.92)的增高,HIV/AIDS晚发现的相对风险越低;而人口密度(RR=1.35,95%CI:1.01,1.81)越大,晚发现风险越高。【结论】兰州市的HIV/AIDS晚发现风险呈上升趋势,并且存在明显的地区差异特征;人均GDP、HIV/AIDS中男性比例和人口密度是HIV/AIDS晚发现的影响因素。因此,对于晚发现风险高和存在相关风险因素的区县,应重视并制定有针对性的HIV筛查和防治服务,降低HIV/AIDS晚发现比例和风险。

“零转介”AIDS诊疗管理模式的临床验证

目的探讨“零转介”诊疗管理模式对人类免疫缺陷病毒(HIV)感染患者治疗疗效和满意度的效果。方法选取石家庄市第五医院2022年7月至12月新增HIV感染者100例,随机分为对照组和研究组,每组50例。对照组给予常规认知干预和健康指导、心理干预,研究组在确诊前较对照组提前7~10 d即给予认知干预、生活指导和心理干预。比较对照组和研究组患者的HIV感染患者睡眠[匹兹堡睡眠质量指数表(PSQI)]、服药依从性、焦虑抑郁量表分值。结果对照组患者的PSQⅠ评分高于研究组患者(P<0.01);研究组患者的焦虑抑郁的心理状况要优于对照组(P<0.05);对照组患者服药依从性良好者比例低于研究组患者(P<0.01);研究组患者对疾病的接受度和认知评定量表均好于对照组患者(P<0.01)。结论“零转介”模式,提前给予认知干预、生活指导和心理干预,可提高HIV感染患者满意度,对社会层面的AIDS预防具有积极意义。

HIV/AIDS合并肝损伤患者的临床特点

目的总结HIV/AIDS合并肝损伤患者的临床特点。方法选取53例HIV抗体阳性合并肝损伤患者作为研究对象,根据肝脏组织病理结果分为病毒性肝炎组(n=27)、肝细胞肝癌组(n=15)和肝脏弥漫大B细胞淋巴瘤组(n=11)。比较三组患者的临床症状和腹部CT影像学特征,以及肝脏组织病理学和免疫表型。结果三组在消瘦、发热、上腹痛、浅表淋巴结肿大、丙氨酸转氨酶升高、碱性磷酸酶升高、甲胎蛋白升高方面差异均有统计学意义;三组在多发结节、单发结节、病灶不均匀强化、病灶轻度强化、病灶重度强化方面差异均有统计学意义(均P<0.05)。病毒性肝炎组镜下见肝小叶结构破坏,肝细胞肝癌组镜下见凝固性坏死组织,弥漫大B细胞淋巴瘤组镜下见肝小叶结构广泛破坏和星空现象。结论不同类型HIV/AIDS合并肝损伤患者临床症状、实验室检查结果、腹部CT影像学特征有一定差异,但其较难区分肝细胞癌和弥漫大B细胞淋巴瘤,两者鉴别诊断需借助肝脏组织病理学和免疫表型方法。

Automated Skin Lesion Diagnosis and Classification Using Learning Algorithms

Due to the rising occurrence of skin cancer and inadequate clinical expertise,it is needed to design Artificial Intelligence(AI)based tools to diagnose skin cancer at an earlier stage.Since massive skin lesion datasets have existed in the literature,the AI-based Deep Learning(DL)modelsfind useful to differentiate benign and malignant skin lesions using dermoscopic images.This study develops an Automated Seeded Growing Segmentation with Optimal EfficientNet(ARGS-OEN)technique for skin lesion segmentation and classification.The proposed ASRGS-OEN technique involves the design of an optimal EfficientNet model in which the hyper-parameter tuning process takes place using the Flower Pollination Algorithm(FPA).In addition,Multiwheel Attention Memory Network Encoder(MWAMNE)based classification technique is employed for identifying the appropriate class labels of the dermoscopic images.A comprehensive simulation analysis of the ASRGS-OEN technique takes place and the results are inspected under several dimensions.The simulation results highlighted the supremacy of the ASRGS-OEN technique on the applied dermoscopic images compared to the recently developed approaches.

Recent advances in computerized imaging and its vital roles in liverdisease diagnosis, preoperative planning, and interventional liversurgery: A review

The earliest and most accurate detection of the pathological manifestations of hepatic diseases ensures effective treatments and thus positive prognostic outcomes.In clinical settings,screening and determining the extent of a pathology are prominent factors in preparing remedial agents and administering approp-riate therapeutic procedures.Moreover,in a patient undergoing liver resection,a realistic preoperative simulation of the subject-specific anatomy and physiology also plays a vital part in conducting initial assessments,making surgical decisions during the procedure,and anticipating postoperative results.Conventionally,various medical imaging modalities,e.g.,computed tomography,magnetic resonance imaging,and positron emission tomography,have been employed to assist in these tasks.In fact,several standardized procedures,such as lesion detection and liver segmentation,are also incorporated into prominent commercial software packages.Thus far,most integrated software as a medical device typically involves tedious interactions from the physician,such as manual delineation and empirical adjustments,as per a given patient.With the rapid progress in digital health approaches,especially medical image analysis,a wide range of computer algorithms have been proposed to facilitate those procedures.They include pattern recognition of a liver,its periphery,and lesion,as well as pre-and postoperative simulations.Prior to clinical adoption,however,software must conform to regulatory requirements set by the governing agency,for instance,valid clinical association and analytical and clinical validation.Therefore,this paper provides a detailed account and discussion of the state-of-the-art methods for liver image analyses,visualization,and simulation in the literature.Emphasis is placed upon their concepts,algorithmic classifications,merits,limitations,clinical considerations,and future research trends.

COMPUTER-AIDED DESIGN OF ADAPTIVE PULSED ARC WELDING OF ROOT WELDS BY MELTING ELECTRODE

The purpose of computer-aided design of new adaptive pulsed arc technologies of welding is: to de- sign optimum algorithms of pulsed control over main energy parameters of welding.It permits:to in- crease welding productivity, to stabilize the welding regime, to control weld formation,taking into ac- count its spatial position, to proveal specie strength of the welded and coatings. Computer- aided design reduces the time of development of new pulsed arc technology:provides the optimization of technological referes according to the operating conditions of welded joints,the prediction of the ser- vice life of the welds.The developed methodology of computer-aided design of advanced technologies, models, original software, adaptive algorithms of pulsed control, and spend equipment permits to regulate penetration,the weld shape, the sizes of heat - affected zone; to predict sired properties and quality of welded joints.

Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection

Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators of malignancy in the early stages of this disease, when mammography is typically used as the screening technology. Computer-Aided Diagnosis (CAD) systems can support the radiologists’ work, by performing a double-reading process, which provides a second opinion that the physician can take into account in the detection process. This paper presents a CAD model based on computer vision procedures for locating suspicious regions that are later analyzed by artificial neural networks, support vector machines and linear discriminant analysis, to classify them into benign or malignant, based on a set of features that are extracted from lesions to characterize their visual content. A genetic algorithm is used to find the subset of features that provide the greatest discriminant power. Our results show that the SVM presented the highest overall accuracy and specificity for classifying microcalcification clusters, while the NN outperformed the rest for mass-classification in the same parameters. Overall accuracy, sensitivity and specificity were measured.

Computer-Aided Design of X-Ray Microtomographic Scanners

The article is to study the development of computer-aided design of X-ray microtomography—the device for investigating the structure and construction of three-dimensional images of organic and inorganic objects on the basis of shadow projections. This article provides basic information regarding CAD of X-ray microtomography and a scheme consisting of three levels. The article also shows basic relations of X-ray computed tomography, the generalized scheme of an X-ray microtomographic scanner. The methods of X-ray imaging of the spatial microstructure and morphometry of materials are described. The main characteristics of an X-ray microtomographic scanner, the X-ray source, X-ray optical elements and mechanical components of the positioning system are shown. The block scheme and software functional scheme for intelligent neural network system of analysis of the internal microstructure of objects are presented. The method of choice of design parameters of CAD of X-ray microtomography aims at improving the quality of design and reducing costs of it. It is supposed to reduce the design time and eliminate the growing number of engineers involved in development and construction of X-ray microtomographic scanners.

Optical diagnosis of colorectal polyps using convolutional neural networks

Colonoscopy remains the gold standard investigation for colorectal cancer screening as it offers the opportunity to both detect and resect pre-malignant and neoplastic polyps.Although technologies for image-enhanced endoscopy are widely available,optical diagnosis has not been incorporated into routine clinical practice,mainly due to significant inter-operator variability.In recent years,there has been a growing number of studies demonstrating the potential of convolutional neural networks(CNN)to enhance optical diagnosis of polyps.Data suggest that the use of CNNs might mitigate the inter-operator variability amongst endoscopists,potentially enabling a“resect and discard”or“leave in”strategy to be adopted in real-time.This would have significant financial benefits for healthcare systems,avoid unnecessary polypectomies of non-neoplastic polyps and improve the efficiency of colonoscopy.Here,we review advances in CNN for the optical diagnosis of colorectal polyps,current limitations and future directions.

Role of artificial intelligence in the diagnosis of oesophageal neoplasia:2020 an endoscopic odyssey

The past decade has seen significant advances in endoscopic imaging and optical enhancements to aid early diagnosis.There is still a treatment gap due to the underdiagnosis of lesions of the oesophagus.Computer aided diagnosis may play an important role in the coming years in providing an adjunct to endoscopists in the early detection and diagnosis of early oesophageal cancers,therefore curative endoscopic therapy can be offered.Research in this area of artificial intelligence is expanding and the future looks promising.In this review article we will review current advances in artificial intelligence in the oesophagus and future directions for development.

PWM VLSI Neural Network for Fault Diagnosis

An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circuit. A simple synapse multiplier is introduced, which has high precision, large linear range and less switching noise effects. A voltage-mode sigmoid circuit with adjustable gain is introduced for realization of different neuron activation functions. A voltage-pulse conversion circuit required for PWM is also introduced, which has high conversion precision and linearity. These 3 circuits are used to design a PWM VLSI neural network circuit to solve noise fault diagnosis for a main bearing. It can classify the fault samples directly. After signal processing, feature extraction and neural network computation for the analog noise signals including fault information,each output capacitor voltage value of VLSI circuit can be obtained, which represents Euclid distance between the corresponding fault signal template and the diagnosing signal, The real-time online recognition of noise fault signal can also be realized.

A robust system for melanoma diagnosis using heterogeneous image databases

Early diagnosis of melanoma is essential for the fight against this skin cancer. Many melanoma detection systems have been developed in recent years. The growth of interest in telemedicine pushes for the development of offsite CADs. These tools might be used by general physicians and dermatologists as a second advice on submission of skin lesion slides via internet. They also can be used for indexation in medical content image base retrieval. A key issue inherent to these CADs is non-heterogeneity of databases obtained with different apparatuses and acquisition techniques and conditions. We hereafter address the problem of training database heterogeneity by developing a robust methodology for analysis and decision that deals with this problem by accurate choice of features according to the relevance of their discriminative attributes for neural network classification. The digitized lesion image is first of all segmented using a hybrid approach based on morphological treatments and active contours. Then, clinical descriptions of malignancy signs are quantified in a set of features that summarize the geometric and photometric features of the lesion. Sequential forward selection (SFS) method is applied to this set to select the most relevant features. A general regression network (GRNN) is then used for the classification of lesions. We tested this approach with color skin lesion images from digitized slides data base selected by expert dermatologists from the hospital “CHU de Rouen-France” and from the hospital “CHU H&#233;di Chaker de Sfax-Tunisia”. The performance of the system is assessed using the index area (Az) of the ROC curve (Receiver Operating Characteristic curve). The classification permitted to have an Az score of 89,10%.

Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework

In past decades,retinal diseases have become more common and affect people of all age grounds over the globe.For examining retinal eye disease,an artificial intelligence(AI)based multilabel classification model is needed for automated diagnosis.To analyze the retinal malady,the system proposes a multiclass and multi-label arrangement method.Therefore,the classification frameworks based on features are explicitly described by ophthalmologists under the application of domain knowledge,which tends to be time-consuming,vulnerable generalization ability,and unfeasible in massive datasets.Therefore,the automated diagnosis of multi-retinal diseases becomes essential,which can be solved by the deep learning(DL)models.With this motivation,this paper presents an intelligent deep learningbased multi-retinal disease diagnosis(IDL-MRDD)framework using fundus images.The proposed model aims to classify the color fundus images into different classes namely AMD,DR,Glaucoma,Hypertensive Retinopathy,Normal,Others,and Pathological Myopia.Besides,the artificial flora algorithm with Shannon’s function(AFA-SF)basedmulti-level thresholding technique is employed for image segmentation and thereby the infected regions can be properly detected.In addition,SqueezeNet based feature extractor is employed to generate a collection of feature vectors.Finally,the stacked sparse Autoencoder(SSAE)model is applied as a classifier to distinguish the input images into distinct retinal diseases.The efficacy of the IDL-MRDD technique is carried out on a benchmark multi-retinal disease dataset,comprising data instances from different classes.The experimental values pointed out the superior outcome over the existing techniques with the maximum accuracy of 0.963.