An ensemble learning integration of multiple CNN with improved vision transformer models for pest classification

Pests are the main threats to crop growth, and the precision classification of pests is conducive to formulating effective prevention and governance strategies. In response to the problems of low efficiency and inadaptability to the large-scale environment of existing pest classification methods, this paper proposes a new pest classification method based on a convolutional neural network (CNN) and an improved Vision Transformer model. First, the MMAlNet is designed to extract the characteristics of the identification object from different scales and finer granularity. Then, a classification model called DenseNet Vision Transformer (DNVT) combining a CNN and an improved vision transformer model is proposed. The proposed DNVT captures both long distance dependencies and local characteristic modelling capabilities, which can effectively improve pest classification accuracy. Finally, the ensemble learning algorithm is used to learn MMAlNet and DNVT classification forecasts for soft voting, further enhancing the classification accuracy of pests. The simulation experiment results on the D0 and IP102 datasets show that the proposed method attained a maximum classification of 99.89 and 74.20%, respectively, which is better than other state-of-the-art methods and has a high practical application value.     

Diseased thyroid tissue classification in OCT images using deep learning: Towards surgical decision support

Intraoperative guidance tools for thyroid surgery based on optical coherence tomography (OCT) could aid distinguish between normal and diseased tissue. However, OCT images are difficult to interpret, thus, real-time automatic analysis could support the clinical decision-making. In this study, several deep learning models were investigated for thyroid disease classification on 2D and 3D OCT data obtained from ex vivo specimens of 22 patients undergoing surgery and diagnosed with several thyroid pathologies. Additionally, two open-access datasets were used to evaluate the custom models. On the thyroid dataset, the best performance was achieved by the 3D vision transformer model with a Matthew's correlation coefficient (MCC) of 0.79 (accuracy = 0.90) for the normal-versus-abnormal classification. On the open-access datasets, the custom models achieved the best performance (MCC > 0.88, accuracy > 0.96). Results obtained for the normal-versus-abnormal classification suggest OCT, complemented with deep learning-based analysis, as a tool for real-time automatic diseased tissue identification in thyroid surgery.     

Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning

In the case of hepatocellular carcinoma (HCC) samples, classification of differentiation is crucial for determining prognosis and treatment strategy decisions. However, a label‐free and automated classification system for HCC grading has not been yet developed. Hence, in this study, we demonstrate the fusion of multiphoton microscopy and a deep‐learning algorithm for classifying HCC differentiation to produce an innovative computer‐aided diagnostic method. Convolutional neural networks based on the VGG‐16 framework were trained using 217 combined two‐photon excitation fluorescence and second‐harmonic generation images; the resulting classification accuracy of the HCC differentiation grade was over 90%. Our results suggest that a combination of multiphoton microscopy and deep learning can realize label‐free, automated methods for various tissues, diseases and other related classification problems.      

Selecting proper combination of mpMRI sequences for prostate cancer classification using multi-input convolutional neuronal network

PurposeThis study aims to develop a deep-learning-based method to classify clinically significant (CS) and clinically insignificant (CiS) prostate cancer (PCa) on multiparametric magnetic resonance imaging (mpMRI) automatically, and to select suitable mpMRI sequences for PCa classification in different anatomic zones.MethodsA multi-input selection network (MISN) is proposed for both PCa classification and the selection of the optimal combination of sequences for PCa classification in a specific zone. MISN is a multi-input/-output classification network consisting of nine branches to process nine input images from the mpMRI data. To improve classification accuracy and reduce model parameters, a pruning strategy is proposed to select a subset of the nine branches of MIST to form two more effective networks for the peripheral zone (PZ) PCa and transition zone (TZ) PCa, which are named as PZN and TZN, respectively. Besides, a new penalized cross-entropy loss function is adopted to train the networks to balance the classification sensitivity and specificity.ResultsThe proposed methods were evaluated on the PROSTATEx challenge dataset and achieved an area under the receiver operator characteristics curve of 0.95, which was much higher than currently published results and ranked first out of more than 1500 entries submitted to the challenge at the time of submission of this paper. For PZ-PCa and TZ-PCa classification, PZN and TZN achieved better performance than MISN.ConclusionsHigher performance can be achieved by selecting a suitable subset of the mpMRI sequences in PCa classification.     

Pattern identification and classification in gene expression data using an autoassociative neural network model

The application of DNA microarray technology for analysis of gene expression creates enormous opportunities to accelerate the pace in understanding living systems and identification of target genes and pathways for drug development and therapeutic intervention. Parallel monitoring of the expression profiles of thousands of genes seems particularly promising for a deeper understanding of cancer biology and the identification of molecular signatures supporting the histological classification schemes of neoplastic specimens. However, the increasing volume of data generated by microarray experiments poses the challenge of developing equally efficient methods and analysis procedures to extract, interpret, and upgrade the information content of these databases. Herein, a computational procedure for pattern identification, feature extraction, and classification of gene expression data through the analysis of an autoassociative neural network model is described. The identified patterns and features contain critical information about gene-phenotype relationships observed during changes in cell physiology. They represent a rational and dimensionally reduced base for understanding the basic biology of the onset of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for the identification and classification of pathological states. The proposed method has been tested on two different microarray datasets-Golub's analysis of acute human leukemia [Golub et al. (1999) Science 286:531-537], and the human colon adenocarcinoma study presented by Alon et al. [1999; Proc Natl Acad Sci USA 97:10101-10106]. The analysis of the neural network internal structure allows the identification of specific phenotype markers and the extraction of peculiar associations among genes and physiological states. At the same time, the neural network outputs provide assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances.     

基于GOCI影像的湖泊悬浮物浓度分类反演

悬浮物直接影响到光在水体中的传播,进而影响着水生生态环境,最终决定了湖泊的初级生产力。传统的遥感反演估算模型大多是针对某一湖区进行统一建模,忽视了不同区域水体光学性质的复杂差异性,并且传统的传感器时间分辨率和空间分辨率受到一定限制。针对太湖、巢湖、滇池、洞庭湖4个湖区利用两步聚类法将高光谱模拟到GOCI影像上的波段进行分类,将水体类型分为三类,第一类水体为悬浮物主导的水体,第二类水体为悬浮物和叶绿素a共同主导的水体,第三类水体为叶绿素a主导的水体。针对不同类型水体的光学特征,分别构建了悬浮物浓度反演模型,结果表明第一类水体可以利用B7/B4,第二和第三类水体可以利用B7/(B8+B4)作为波段组合因子对悬浮物浓度进行模型构建。精度验证结果表明,分类建模后第一类和第三类水体悬浮物浓度估算精度都得到了较明显提高,第一类水体RMSE降低了9.19mg/L,MAPE降低了3%,第三类水体RMSE降低了5.63 mg/L,MAPE降低了13.97%,第二类水体精度稍有降低。最后将反演模型应用于2013年5月13日的GOCI影像,可知整体而言太湖西南部地区悬浮物浓度较高,东北部地区悬浮物浓度较低,并且从9:00到15:00,太湖南部悬浮物浓度较高的区域在逐渐缩小。     

Computer-aided diagnosis of breast cancer using cytological images: A systematic review

Cytological evaluation by microscopic image-based characterization [imprint cytology (IC) and fine needle aspiration cytology (FNAC)] plays an integral role in primary screening/detection of breast cancer. The sensitivity of IC and FNAC as a screening tool is dependent on the image quality and the pathologist’s level of expertise. Computer-aided diagnosis (CAD) is used to assists the pathologists by developing various machine learning and image processing algorithms. This study reviews the various manual and computer-aided techniques used so far in breast cytology. Diagnostic applications were studied to estimate the role of CAD in breast cancer diagnosis. This paper presents an overview of image processing and pattern recognition techniques that have been used to address several issues in breast cytology-based CAD including slide preparation, staining, microscopic imaging, pre-processing, segmentation, feature extraction and diagnostic classification. This review provides better insights to readers regarding the state of the art the knowledge on CAD-based breast cancer diagnosis to date.     

Convolutional neural network‐based malaria diagnosis from focus stack of blood smear images acquired using custom‐built slide scanner

The present paper introduces a focus stacking‐based approach for automated quantitative detection of Plasmodium falciparum malaria from blood smear. For the detection, a custom designed convolutional neural network (CNN) operating on focus stack of images is used. The cell counting problem is addressed as the segmentation problem and we propose a 2‐level segmentation strategy. Use of CNN operating on focus stack for the detection of malaria is first of its kind, and it not only improved the detection accuracy (both in terms of sensitivity [97.06%] and specificity [98.50%]) but also favored the processing on cell patches and avoided the need for hand‐engineered features. The slide images are acquired with a custom‐built portable slide scanner made from low‐cost, off‐the‐shelf components and is suitable for point‐of‐care diagnostics. The proposed approach of employing sophisticated algorithmic processing together with inexpensive instrumentation can potentially benefit clinicians to enable malaria diagnosis.      

CO-WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image

The most significant groupings of cold-blooded creatures are the fish family. It is crucial to recognize and categorize the most significant species of fish since various species of seafood diseases and decay exhibit different symptoms. Systems based on enhanced deep learning can replace the area's currently cumbersome and sluggish traditional approaches. Although it seems straightforward, classifying fish images is a complex procedure. In addition, the scientific study of population distribution and geographic patterns is important for advancing the field's present advancements. The goal of the proposed work is to identify the best performing strategy using cutting-edge computer vision, the Chaotic Oppositional Based Whale Optimization Algorithm (CO-WOA), and data mining techniques. Performance comparisons with leading models, such as Convolutional Neural Networks (CNN) and VGG-19, are made to confirm the applicability of the suggested method. The suggested feature extraction approach with Proposed Deep Learning Model was used in the research, yielding accuracy rates of 100 %. The performance was also compared to cutting-edge image processing models with an accuracy of 98.48 %, 98.58 %, 99.04 %, 98.44 %, 99.18 % and 99.63 % such as Convolutional Neural Networks, ResNet150V2, DenseNet, Visual Geometry Group-19, Inception V3, Xception. Using an empirical method leveraging artificial neural networks, the Proposed Deep Learning model was shown to be the best model.     

Rapid identification of human ovarian cancer in second harmonic generation images using radiomics feature analyses and tree‐based pipeline optimization tool

Ovarian cancer is currently one of the most common cancers of the female reproductive organs, and its mortality rate is the highest among all types of gynecologic cancers. Rapid and accurate classification of ovarian cancer plays an important role in the determination of treatment plans and prognoses. Nevertheless, the most commonly used classification method is based on histopathological specimen examination, which is time‐consuming and labor‐intensive. Thus, in this study, we utilize radiomics feature extraction methods and the automated machine learning tree‐based pipeline optimization tool (TOPT) for analysis of 3D, second harmonic generation images of benign, malignant and normal human ovarian tissues, to develop a high‐efficiency computer‐aided diagnostic model. Area under the receiver operating characteristic curve values of 0.98, 0.96 and 0.94 were obtained, respectively, for the classification of the three tissue types. Furthermore, this approach can be readily applied to other related tissues and diseases, and has great potential for improving the efficiency of medical diagnostic processes.     

Gene regulatory network from microarray data of colon cancer patients using TSK-type recurrent neural fuzzy network

In this work we applied a TSK-type recurrent neural fuzzy approach to extract regulatory relationship among genes and reconstruct gene regulatory network from microarray data. The identified signature has captured the regulatory relationship among 27 differentially expressed genes from microarray dataset. We applied three different methods viz., feed forward neural fuzzy, modified genetic algorithm and recurrent neural fuzzy, on the same data set for the inference of GRNs and the results obtained are almost comparable. In all tested cases, TRNFN identified more biologically meaningful relations. We found that 87.8% of the total interactions extracted by TRNFN are correct in accordance with the biological knowledge. Our analysis resulted in 2 major outcomes. First, upregulated genes are regulated by more genes than downregulated genes. Second, tumor activators activate other tumor activators and suppress tumor suppressers strongly in the disease environment. These findings will help to elucidate the common molecular mechanism of colon cancer, and provide new insights into cancer diagnostics, prognostics and therapy.