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

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

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

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.

Image processing plays a significant role in various fields like military, business, healthcare and science. Ultrasound (US), Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the various image tests used in the treatment of the cancer. Detecting the liver tumor by these tests is a complex process. Hence, in this research work, a novel approach utilizing a deep learning model is used. That is Deep Belief Network (DBN) with Opposition-Based Learning (OBL)-Grey Wolf Optimization (GWO) is used for the classification of liver cancer. This process undergoes five major processes. Initially, in pre-processing the color contrast is improved by Contrast Limited Adaptive Histogram Equalization (CLAHE) and the noise is removed by Wiener Filtering (WF). The liver is segmented by adaptive thresholding following pre-processing. Following that, the kernelizedFuzzy C Means (FCM) method is used to segment the tumor area. The form, color, and texture features are then extracted during the feature extraction process. Finally, these traits are categorized using DBN, and OBL-GWO is employed to enhance system performance. The entire evaluation is done on Liver Tumor Segmentation (LiTS) benchmark dataset. Finally, the performance of the proposed DBN-OBL-GWO is compared to other models and their achievements are proved. The proposed DBN-OBL-GWO achieves a better accuracy of 0.995, precision of 0.948 and false positive rate (FPR) of 0.116, respectively.

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.

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.

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.

The detection and treatment of cancerous tumors in the liver are considered one of the most challenging health issues for patients globally. There is a need for fully automated systems that can precisely detect and segment the tumorous regions in medical images. This work aims to develop an automated system which utilizes a hybrid- deep- learning framework with fusion multi-scale inception feature extraction called HSCS-NET (Hawk-Swarm Ception Segmentation Network). In the proposed model, the encoder comprises of Hawk Gating with SE (squeeze-and-excitation) attention while decoders consist of adaptive attention skip fusion which comprises of Swarm ception Residual ASPP bridges. These components allow the model to recover important details of the boundaries of the tumors irrespective to their shape, size, and the tissue contrast in CT scans.In order to improve segmentation, the HSCS-NET framework is equipped with a hybrid optimization module based on PSO (Particle Swarm Optimization) and GWO (Grey Wolf Optimization) for dynamic feature selection and reliable convergence. The model was evaluated against the 3DIRCADb1 Liver Tumor Segmentation Challenge (LiTS) dataset and significantly outperformed all other models achieving a Dice coefficient value of 0.98, Accuracy of 0.9891, and Precision of 0.9901.This marks a substantial improvement over prior models such as Christ et al.’s CNN (Dice: 0.823), Wu et al.’s Fuzzy C-means + GC (Dice: 0.83), Muhammad et al.’s ResNet (Dice: 0.87, Accuracy: 0.945, Precision: 0.93), and Kaur et al.’s PSO-PSP-Net (Accuracy: 0.9754, Precision: 0.9632).The HSCS-NET architecture is mathematically grounded, modularly extensible, and validated through rigorous cross-validation and confidence refinement. With its high segmentation performance, clinical reliability, and computational efficiency, HSCS-NET stands as a superior advancement in automated liver cancer diagnostics, reducing clinician workload and improving patient prognoses through precision imaging.

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.

Background: Coronary Artery Disease (CAD) is one of the biggest causes of mortality worldwide. Risk stratification for early detection is essential for the primary prevention of CAD. QRISK3 is known to overestimate future CAD risk in some populations, resulting in unnecessary preventive treatment and reduced cost-effectiveness and safety. Combining machine learning model with the metaheuristic optimisation approach using the PSO algorithm may outperform QRISK3 in predicting CAD. It improves performance by selecting the best-performing subset of features related to clinical outcomes. Research Question: Does the performance of Machine Learning Models Combined with PSO Algorithm for Feature Selection as a Metaheuristic Optimisation Approach in Predicting Coronary Arterial Disease using the UK Biobank dataset outperform the QRISK3 calculator? Aims: This study is to assess the accuracy of QRISK3 in predicting CAD using the UK Biobank dataset. It aims to evaluate the efficacy of machine learning models on the identical dataset for predicting CAD. The work utilises the PSO algorithm for feature selection to identify the optimal subset of features from the UK Biobank dataset. Methods: This study utilises data from the UK Biobank. The dataset consists of 348,015 participants aged 24-84 with no prior diagnosis of CAD. The performance of both QRISK3 and machine learning models was evaluated separately using ROC analysis. Several machine learning models were employed: Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, and Gradient Boosting. The dataset was split into training and test sets with a ratio of 4:1 for the machine learning models. Each model has been developed by adding a PSO algorithm to enhance the model's classification accuracy. Results: Out of the total 348,015 participants, 23,136 individuals (6.64%) were diagnosed with CAD within 10 years following their first visit, while 324,879 individuals (93.4%) did not develop CAD. The AUC value of the QRISK3 prediction is 0.6113, while the combined machine learning models using the PSO algorithm using the gradient boosting model achieve an AUC of 0.7258, showing better performance. Conclusions: This study shows hybrid machine learning models optimised with the PSO algorithm can better predict CAD than QRISK3. These ML models can effectively identify high-risk CAD patients, allowing for more personalised preventative strategies and supporting policymakers in implementing lifestyle change recommendations.

TL-GWO: Fine-tuned transfer learning with grey wolf optimizer for accurate fundus image-based eye disease classification.

Accurate liver tumor segmentation is critical for the diagnosis and treatment of liver cancer, yet remains challenging due to image quality and tumor heterogeneity. Existing methods predominantly focus on single-phase and single-time-point CT scans, neglecting complementary multi-phase information and longitudinal correlations. In this paper, we propose the first multi-modal multi-temporal network, which integrates diverse information from arterial phase and longitudinal data to facilitate liver tumor segmentation of venous phase. Our approach mainly comprises three novel blocks: (1) A polynomial residual block that captures abundant tumor characteristics via nonlinear hierarchical feature learning; (2) A longitudinal phase attention block that refines input features from channel and spatial dimensions, and recalibrates the target feature based on inter-phase and inter-temporal dependencies; (3) A progressive up-sampling block that preserves structural details during resolution restoration, and forms an effective decoder architecture. Experiments on a large-scale CT dataset of longitudinal multi-phase liver tumors demonstrate that the proposed network achieves promising liver tumor segmentation and outperforms state-of-the-art methods. In addition, an ablation study validates the functions of the proposed blocks.