Liver Tumor Classification Using Optimal Opposition-Based Grey Wolf Optimization

The incidence of liver tumors is among the top three in China. The treatments of benign and malignant tumors are different. Accurate diagnosis plays an important role in guiding the treatment of tumors. The aim of this study is to solve the following: (1) blurred boundary between the liver tumor and other organs causes incorrect segmentation of liver tumor boundaries; (2) large difference in tumor size and the diversity in texture and grayscale are major challenges in liver tumor classification tasks. Firstly, the liver tumor is segmented from the original CT images by a tumor segmentation network, UNet++ with fusion loss and atrous spatial pyramid pooling (FLAS-UNet++). The proposed segmentation method can solve the problem of tumor edge segmentation error by learning the tumor edge information. Secondly they are adaptively cropped according to the tumor volume to reduce the over-fitting and over-sensitivity of the deep network. Thirdly an improved Dense Block is designed to pay more attention to the changes in grayscale and texture between benign and malignant tumors. Finally, the features extracted from the network combined with tumor volume, patient's sex and age, are sent to a classifier for diagnosis. Liver tumor segmentation results show that the dice, HD95 reached 71.9%, 12.1 mm, respectively. The classification results show that the accuracy, specificity, sensitivity and area under curve reached 82.4%, 79.8%, 84.4%, 87.5%, respectively. The segmentation and classification results are both better than other's methods and mainstream networks. In order to solve existing problems of liver tumor CT image classification methods, our method realizes the accurate segmentation and classification of liver tumors in CT images and has important clinical application value.

In recent times, the detection of brain tumour is a common fatality in the field of the health community. Generally, the brain tumor is an abnormal mass of tissue where the cells grow up and increase uncontrollably, apparently unregulated by mechanisms that control cells. A number of techniques have been developed so far; however, the time consumption in detecting brain tumor is still a challenge in the field of image processing. This paper intends to propose a new detection model even accurately. The model includes certain processes like Preprocessing, Segmentation, Feature Extraction and Classification. Particularly, two extreme processes like contrast enhancement and skull stripping are processed under initial phase, in the segmentation process, this paper uses Fuzzy Means Clustering (FCM) algorithm. Both Gray Level Co-occurrence Matrix (GLCM) as well as Gray-Level Run-Length Matrix (GRLM) features are extracted in feature extraction phase. Moreover, this paper uses Deep Belief Network (DBN) for classification. The DBN is integrated with the optimization approach, and hence this paper introduces the optimized DBN, for which Grey Wolf Optimization (GWO) is used here. The proposed model is termed as GW-DBN model. The proposed model compares its performance over other conventional methods in terms of Accuracy, Specificity, Sensitivity, Precision, Negative Predictive Value (NPV), F 1 Score and Matthews Correlation Coefficient (MCC), False negative rate (FNR), False positive rate (FPR) and False Discovery Rate (FDR), and proven the superiority of proposed work.

Cancer plays a major risk for public health worldwide. According to the survey made by the cancer society predicts approximate about 42,220 new cases will be diagnosed and around 32,220 people will die of this cancer that is around 71% of people will die in 2018 and Liver cancer rate is increased by 3% for every year since 2000 and achieved second leading place for the cause of death. There is a con-tinuous in the development with regard to prevent and different options for treating the cancer. Detection of cancer at its initial stages is very difficult with the help of pathological information’s, so as any added support CAD systems using CT scan images are being designed from few decades in order to find out cancer in its early stage. In this paper discussed various segmentation techniques and liver tumor detection techniques to initial segment out the liver region from the abdominal and then to extract the efficient characteristics. Based on the characteristics presences of tumour is identified and separated out from the liver and finally analyse the stage of the cancer. Therefore the process is divided into three parts; 1.Region segmentation, 2.Liver Tumour segmentation and 3.Detection of Cancer stage. In this paper, study is done on different methods of liver region and tumour segmentation of abdominal CT scan to analyze liver tumor and detection of early stage of the tumor

Liver cancer is a life threatening illness and one of the fastest growing cancer in the world.Segmenting liver tumors from computed tomography(CT) images is crucial for the diagnosis and management of liver cancer.The variety of appearances, hazy borders, variable densities, forms and sizes of lesions of tumors makes it challenging .This work introduces a liver tumor segmentation method based on the Liver Tumor Segmentation (LITS) dataset.U-net model is used to segment the lesions in the tumor.A convolutional neural network architecture created especially for semantic segmentation problems is used to implement a Unet model for liver tumor segmentation.U-Net and Seg-Net models demonstrate better segmentation performance by utilizing the extensive annotated data present in the LITS dataset.To produce the segmentation output , the encoder down samples the input picture through convolutional and pooling layers, while the decoder up samples the feature map.The last layer is sigmoid activation function the task is likely binary segmentation.The outcomes of the experiment confirm the efficacy of the suggested method, demonstrating its potential to improve diagnostic precision in medical environments.Dice-coefficient, Dice-loss,F1 score are the metrics used to calculate the performance of the segmented images

Hepatocellular carcinoma currently causes over 800 000 fatalities per year worldwide – and the number of cases is increasing. An early diagnosis and treatment play a crucial role in saving patients’ lives. The purpose of this study is the exploration of a robust and precise computer-aided diagnosis (CAD) method using deep learning algorithms for liver tumor localization and segmentation. The difficulty of liver tumor segmentation lies within the recognition of the contrast between healthy and malignant tissues. This study proposes an implementation of a two-phased multi-scale and multi-resolution training pipeline to perform high accuracy in medical imaging segmentation tasks. For the experiments, the Liver Tumor Segmentation challenge (LiTS) public dataset was used. It contains 131 computed tomography (CT) images, out of which 82% show liver tumors with various shapes of lesion distribution. The final results show a dice per case score of 96.3% for liver segmentation and 72.5% for tumor segmentation when compared to the top LiTS results.

Accurate segmentation of the liver and liver tumour (LT) is challenging due to its hazy boundaries and large shape variability. Although using U-Net for liver and LT segmentation achieves better results than manual segmentation, it loses spatial and channel features during segmentation, leading to inaccurate liver and LT segmentation. A residual deformable split depth-wise separable U-Net (RDSDSU-Net) is proposed to increase the accuracy of liver and LT segmentation. The residual deformable convolution layer (DCL) with deformable pooling (DP) is used in the encoder as an attention mechanism to adaptively extract liver and LT shape and position characteristics. Afterward, a convolutional spatial and channel features split graph network (CSCFSG-Net) is introduced in the middle processing layer to improve the expression capability of the liver and LT features by capturing spatial and channel features separately and to extract global contextual liver and LT information from spatial and channel features. Sub-pixel convolutions (SPC) are used in the decoder section to prevent the segmentation results from having a chequerboard artefact effect. Also, the residual deformable encoder features are combined with the decoder through summation to avoid increasing the number of feature maps (FM). Finally, the efficiency of the RDSDSU-Net is evaluated on the 3DIRCADb and LiTS datasets. The DICE score of the proposed RDSDSU-Net achieved 98.21% for liver segmentation and 93.25% for LT segmentation on 3DIRCADb. The experimental outcomes illustrate that the proposed RDSDSU-Net model achieved better segmentation results than the existing techniques.

PSO-PSP-Net + InceptionV3: An optimized hyper-parameter tuned Computer-Aided Diagnostic model for liver tumor detection using CT scan slices

Objective: Segmentation of liver and liver tumors from 3D medical images is a challenging and computationally expensive task. Organs that are in close proximity may have similar shape, texture, and intensity, which makes it difficult for accurate segmentation. Accurate segmentation of liver tumors is important for diagnosis and treatment planning of liver cancer. Methods: A hybrid model with a U-Net based structure and the Whale Optimization Algorithm (WOA) was proposed. WOA was used to optimize the hyperparameters of the conventional LiTS-Res-UNet to obtain the best segmentation performance of the deep learning model. Results: The LiTS-Res-Unet + WOA hybrid model achieved a performance of 99.54% for accuracy, with a Dice coefficient of 92.38% and a Jaccard index of 86.73% on the benchmark dataset, outperforming state-of-the-art methods. Conclusions: The WOA-based adaptive search space was able to obtain an optimal set of hyperparameters for deep learning model convergence while increasing the accuracy of the model in the proposed hybrid model. The robust performance and clinical applicability of the model in liver tumor segmentation were demonstrated.

Given the rich and complementary information contained in multi-phase CT images (CTs), they play an indispensable role in liver cancer diagnosis and prognosis, wherein an important prerequisite is liver tumor segmentation. However, spatial misalignments across phases and the limited availability of high-quality multi-phase CT datasets significantly hinder the performance of liver tumor segmentation. To tackle these challenges, we here propose SynLiTS, a novel multi-phase liver tumor segmentation framework. Its core idea is to synthesize multi-phase CTs with strictly-aligned liver tumors based on pseudo-normal multi-phase CTs. Specifically, an FFC-based Inpainter is first designed to generate pseudo-normal CTs by reconstructing dilated liver tumors. The pseudo-normal CTs and randomly generated tumor masks are then combined via a phase prompting-driven diffusion model to synthesize multi-phase liver tumor CTs with diverse tumor characteristics. In this way,multi-phase CTs with perfectly-aligned liver tumor labels are obtained. We also construct a real multi-phase liver tumor dataset, named MPLiTS. Finally, the synthesized and real multi-phase CTs are used to train a liver tumor segmentation model, which incorporates a context-aware fusion module to effectively learn and integrate multi-phase information. SynLiTS is evaluated on both internal and external datasets, and the results show that it outperforms state-of-the-art methods by large margins. Code will be released at https://github.com/Chyiun/SynLiTS.

Diabetic retinopathy (DR) is the major ophthalmic disorder because of variation in veins structure which may cause blindness. The retinal vein morphology distinguishes the progressive phases of various sight debilitating maladies and consequently clears an approach to characterize its seriousness. The proposed method for retinal blood vessels detection consists of two major processes: denoising and vasculature segmentation. First, we used denoising preprocessing steps which comprises of Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement along with morphological filters to remove low frequency noise, followed by masking to excerpt Region Of Interest (ROI) and difference image of low pass filter to suppress high frequency noise. Adaptive thresholding has been used for vessels segmentation, followed by postprocessing to eliminate unconnected pixels and to obtain Vessel Location Map (VLM). Dilate operation has been used to enhance vessels diameter. In the second step, Combination Of Shifted Filter Responses (B-COSFIRE) for vasculature enhancement along with adaptive thresholding has been applied to segment vessel and background pixels. B represents the bar/vessel like structure. Finally, using pixel by pixel AND operation between VLM and the output of adaptive thresholding, to obtain desired binary image. The proposed framework has been validated on DRIVE and STARE images datasets and compared with other recent approaches for retinal blood vessels segmentation. The proposed scheme provides good results in the term of Accuracy (Acc), Sensitivity (Se) and Specificity (Sp) as compared to other competing methods.

Liver tumor segmentation on computed tomography slices is a very difficult task because the medical images are often corrupted by noise and sampling artifacts. Besides, liver tumors are often surrounded by other abdominal structures with similar densities. Therefore, they often show the phenomenon of intensity inhomogeneity. These restrict the liver tumor segmentation. People tried to use traditional level set methods to segment the liver tumor, but the results were not satisfying due to the noise and the low gradient response on the liver tumor boundary. In this paper, we propose a multidistribution level set method which can overcome the insufficient segmentation and over-segmentation problems. We have done many experiments and compared our approach with the CV model and LSACM model. We also use the proposed method to segment the public data set from the "3D Liver Tumor Segmentation Challenge". All results reveal that our method is better even for liver tumors with low contrast and blurred boundaries.

Weakly-Supervised teacher-Student network for liver tumor segmentation from non-enhanced images.

Tea cultivation holds significant economic and agricultural value across many regions, particularly in developing countries, where it supports millions of livelihoods. However, the productivity and quality of tea leaves are severely threatened by a wide range of foliar diseases, which, if undetected or misdiagnosed, can lead to substantial crop losses. Existing manual and traditional image-based detection methods are time-consuming and often lack the precision required for early-stage identification across multiple disease types. To address these limitations, this study proposes TeaLeafNet-GWO, an intelligent deep learning framework that integrates a hybrid CNN–Transformer architecture with Gray Wolf Optimization (GWO) for automated tea leaf disease classification. The model combines the spatial feature extraction power of CNNs with the global contextual awareness of Transformer blocks (MobileViT), enhanced by channel-wise recalibration using a squeeze-and-excitation network (SENet). GWO is employed to fine-tune critical hyperparameters, ensuring optimal performance and generalization. The dataset comprises 999 high-resolution images representing eight distinct tea leaf conditions, including both healthy and diseased classes. Preprocessing techniques such as contrast-limited adaptive histogram equalization (CLAHE) and median filtering were applied to enhance image quality and suppress noise. The model was trained and evaluated using a stratified split (70% training, 30% testing). TeaLeafNet-GWO achieved an average accuracy of 97.72%, F1-score of 86.66%, and Matthews correlation coefficient (MCC) of 84.44% in the training phase, with similarly high performance in testing, outperforming several state-of-the-art hybrid models in comparative analysis. These results demonstrate TeaLeafNet-GWO’s effectiveness as a lightweight, scalable, and accurate solution for real-time disease monitoring, offering tangible benefits to precision agriculture and smart farming systems.

PurposeUp to date development in sentiment analysis has resulted in a symbolic growth in the volume of study, especially on more subjective text types, namely, product or movie reviews. The key difference between these texts with news articles is that their target is defined and unique across the text. Hence, the reviews on newspaper articles can deal with three subtasks: correctly spotting the target, splitting the good and bad content from the reviews on the concerned target and evaluating different opinions provided in a detailed manner. On defining these tasks, this paper aims to implement a new sentiment analysis model for article reviews from the newspaper.Design/methodology/approachHere, tweets from various newspaper articles are taken and the sentiment analysis process is done with pre-processing, semantic word extraction, feature extraction and classification. Initially, the pre-processing phase is performed, in which different steps such as stop word removal, stemming, blank space removal are carried out and it results in producing the keywords that speak about positive, negative or neutral. Further, semantic words (similar) are extracted from the available dictionary by matching the keywords. Next, the feature extraction is done for the extracted keywords and semantic words using holoentropy to attain information statistics, which results in the attainment of maximum related information. Here, two categories of holoentropy features are extracted: joint holoentropy and cross holoentropy. These extracted features of entire keywords are finally subjected to a hybrid classifier, which merges the beneficial concepts of neural network (NN), and deep belief network (DBN). For improving the performance of sentiment classification, modification is done by inducing the idea of a modified rider optimization algorithm (ROA), so-called new steering updated ROA (NSU-ROA) into NN and DBN for weight update. Hence, the average of both improved classifiers will provide the classified sentiment as positive, negative or neutral from the reviews of newspaper articles effectively.FindingsThree data sets were considered for experimentation. The results have shown that the developed NSU-ROA + DBN + NN attained high accuracy, which was 2.6% superior to particle swarm optimization, 3% superior to FireFly, 3.8% superior to grey wolf optimization, 5.5% superior to whale optimization algorithm and 3.2% superior to ROA-based DBN + NN from data set 1. The classification analysis has shown that the accuracy of the proposed NSU − DBN + NN was 3.4% enhanced than DBN + NN, 25% enhanced than DBN and 28.5% enhanced than NN and 32.3% enhanced than support vector machine from data set 2. Thus, the effective performance of the proposed NSU − ROA + DBN + NN on sentiment analysis of newspaper articles has been proved.Originality/valueThis paper adopts the latest optimization algorithm called the NSU-ROA to effectively recognize the sentiments of the newspapers with NN and DBN. This is the first work that uses NSU-ROA-based optimization for accurate identification of sentiments from newspaper articles.

Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. While various algorithms for delineating organ-at-risks (OARs) and tumor targets have been proposed, automatic segmentation of livers and liver tumors remains intractable due to their low tissue contrast with respect to the surrounding organs and their deformable shape in CT images. The U-Net has gained increasing popularity recently for image analysis tasks and has shown promising results. Conventional U-Net architectures, however, suffer from three major drawbacks. First, skip connections allow for the duplicated transfer of low resolution information in feature maps to improve efficiency in learning, but this often leads to blurring of extracted image features. Secondly, high level features extracted by the network often do not contain enough high resolution edge information of the input, leading to greater uncertainty where high resolution edge dominantly affects the network's decisions such as liver and liver-tumor segmentation. Thirdly, it is generally difficult to optimize the number of pooling operations in order to extract high level global features, since the number of pooling operations used depends on the object size. To cope with these problems, we added a residual path with deconvolution and activation operations to the skip connection of the U-Net to avoid duplication of low resolution information of features. In the case of small object inputs, features in the skip connection are not incorporated with features in the residual path. Furthermore, the proposed architecture has additional convolution layers in the skip connection in order to extract high level global features of small object inputs as well as high level features of high resolution edge information of large object inputs. Efficacy of the modified U-Net (mU-Net) was demonstrated using the public dataset of Liver tumor segmentation (LiTS) challenge 2017. For liver-tumor segmentation, Dice similarity coefficient (DSC) of 89.72 %, volume of error (VOE) of 21.93 %, and relative volume difference (RVD) of - 0.49 % were obtained. For liver segmentation, DSC of 98.51 %, VOE of 3.07 %, and RVD of 0.26 % were calculated. For the public 3D Image Reconstruction for Comparison of Algorithm Database (3Dircadb), DSCs were 96.01 % for the liver and 68.14 % for liver-tumor segmentations, respectively. The proposed mU-Net outperformed existing state-of-art networks.