Analysis of liver cancer detection based on image processing

Medical imaging is the most important tool for detecting complications in the inner body of medicine. Nowadays, with the development of image processing technology as well as changing the size of photos to higher resolution images in the field of digital medical imaging, there is an efficient and accurate system for segmenting this. Real-world images that for a variety of reasons have poor heterogeneity, noise and contrast are essential. Digital image segmentation in medicine is used for diagnostic and therapeutic analysis, which is very helpful for physicians. In this study, we aim at liver cancer photographs, which aim to more accurately detect the lesion or tumor of the liver because accurate and timely detection of the tumor is very important in the survival and life of the patient.The aim of this paper is to simplify the obnoxious study problems related to the study of MR images. The liver is the second organ most generic involved by metastatic disease being liver cancer one of the prominent causes of death worldwide. Without healthy liver a person cannot survive. It is life threatening disease which is very challenging perceptible for both medical and engineering technologists. Medical image processing is used as a non-invasive method to detect tumours. The chances of survival having liver Tumor highly depends on early detection of Tumor and then classification as cancerous and noncancerous tumours. Image processing techniques for automatic detection of brain are includes pre-processing and enhancement, image segmentation, classification and volume calculation, Poly techniques have been developed for the detection of liver Tumor and different liver toM oR detection algorithms and methodologies utilized for Tumor diagnosis. Novel methodology for the detection and diagnosis of liver Tumor.

Improving Prostate Cancer Detection with Breast Histopathology Images

Deep neural networks have introduced significant advancements in the field of machine learning-based analysis of digital pathology images including prostate tissue images. With the help of transfer learning, classification and segmentation performance of neural network models have been further increased. However, due to the absence of large, extensively annotated, publicly available prostate histopathology datasets, several previous studies employ datasets from well-studied computer vision tasks such as ImageNet dataset. In this work, we propose a transfer learning scheme from breast histopathology images to improve prostate cancer detection performance. We validate our approach on annotated prostate whole slide images by using a publicly available breast histopathology dataset as pre-training. We show that the proposed cross-cancer approach outperforms transfer learning from ImageNet dataset.

An automated end-to-end deep learning-based framework for lung cancer diagnosis by detecting and classifying the lung nodules

Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection is crucial for improving patient outcomes. Nevertheless, early diagnosis of cancer is a major challenge, particularly in low-resource settings where access to medical resources and trained radiologists is limited. The objective of this study is to propose an automated end-to-end deep learning-based framework for the early detection and classification of lung nodules, specifically for low-resource settings. The proposed framework consists of three stages: lung segmentation using a modified 3D U-Net named 3D Res-U-Net, nodule detection using YOLO-v5, and classification with a Vision Transformer-based architecture. We evaluated the proposed framework on a publicly available dataset, LUNA16. The proposed framework's performance was measured using the respective domain's evaluation matrices. The proposed framework achieved a 98.82% lung segmentation dice score while detecting the lung nodule with 0.76 mAP@50 from the segmented lung, at a low false-positive rate. The performance of both networks of the proposed framework was compared with other studies and found to outperform them regarding segmentation and detection accuracy. Additionally, our proposed Vision transformer network obtained an accuracy of 93.57%, which is 1.21 higher than the state-of-the-art networks. Our proposed end-to-end deep learning-based framework can effectively segment lungs, and detect and classify lung nodules, specifically in low-resource settings with limited access to radiologists. The proposed framework outperforms existing studies regarding all the respective evaluation metrics. The proposed framework can potentially improve the accuracy and efficiency of lung cancer screening in low-resource settings, ultimately leading to better patient outcomes.

Sources of performance variability in deep learning-based polyp detection

Validation metrics are a key prerequisite for the reliable tracking of scientific progress and for deciding on the potential clinical translation of methods. While recent initiatives aim to develop comprehensive theoretical frameworks for understanding metric-related pitfalls in image analysis problems, there is a lack of experimental evidence on the concrete effects of common and rare pitfalls on specific applications. We address this gap in the literature in the context of colon cancer screening. Our contribution is twofold. Firstly, we present the winning solution of the Endoscopy computer vision challenge (EndoCV) on colon cancer detection, conducted in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2022. Secondly, we demonstrate the sensitivity of commonly used metrics to a range of hyperparameters as well as the consequences of poor metric choices. Based on comprehensive validation studies performed with patient data from six clinical centers, we found all commonly applied object detection metrics to be subject to high inter-center variability. Furthermore, our results clearly demonstrate that the adaptation of standard hyperparameters used in the computer vision community does not generally lead to the clinically most plausible results. Finally, we present localization criteria that correspond well to clinical relevance. Our work could be a first step towards reconsidering common validation strategies in automatic colon cancer screening applications.

New pyramidal hybrid textural and deep features based automatic skin cancer classification model: Ensemble DarkNet and textural feature extractor

Background: Skin cancer is one of the widely seen cancer worldwide and automatic classification of skin cancer can be benefited dermatology clinics for an accurate diagnosis. Hence, a machine learning-based automatic skin cancer detection model must be developed. Material and Method: This research interests to overcome automatic skin cancer detection problem. A colored skin cancer image dataset is used. This dataset contains 3297 images with two classes. An automatic multilevel textural and deep features-based model is presented. Multilevel fuse feature generation using discrete wavelet transform (DWT), local phase quantization (LPQ), local binary pattern (LBP), pre-trained DarkNet19, and DarkNet53 are utilized to generate features of the skin cancer images, top 1000 features are selected threshold value-based neighborhood component analysis (NCA). The chosen top 1000 features are classified using the 10-fold cross-validation technique. Results: To obtain results, ten-fold cross-validation is used and 91.54% classification accuracy results are obtained by using the recommended pyramidal hybrid feature generator and NCA selector-based model. Further, various training and testing separation ratios (90:10, 80:20, 70:30, 60:40, 50:50) are used and the maximum classification rate is calculated as 95.74% using the 90:10 separation ratio. Conclusions: The findings and accuracies calculated are denoted that this model can be used in dermatology and pathology clinics to simplify the skin cancer detection process and help physicians.

Breast cancer detection using artificial intelligence techniques: A systematic literature review

Cancer is one of the most dangerous diseases to humans, and yet no permanent cure has been developed for it. Breast cancer is one of the most common cancer types. According to the National Breast Cancer foundation, in 2020 alone, more than 276,000 new cases of invasive breast cancer and more than 48,000 non-invasive cases were diagnosed in the US. To put these figures in perspective, 64% of these cases are diagnosed early in the disease's cycle, giving patients a 99% chance of survival. Artificial intelligence and machine learning have been used effectively in detection and treatment of several dangerous diseases, helping in early diagnosis and treatment, and thus increasing the patient's chance of survival. Deep learning has been designed to analyze the most important features affecting detection and treatment of serious diseases. For example, breast cancer can be detected using genes or histopathological imaging. Analysis at the genetic level is very expensive, so histopathological imaging is the most common approach used to detect breast cancer. In this research work, we systematically reviewed previous work done on detection and treatment of breast cancer using genetic sequencing or histopathological imaging with the help of deep learning and machine learning. We also provide recommendations to researchers who will work in this field

CT Multi-Task Learning with a Large Image-Text (LIT) Model

Large language models (LLM) not only empower multiple language tasks but also serve as a general interface across different spaces. Up to now, it has not been demonstrated yet how to effectively translate the successes of LLMs in the computer vision field to the medical imaging field which involves high-dimensional and multi-modal medical images. In this paper, we report a feasibility study of building a multi-task CT large image-text (LIT) model for lung cancer diagnosis by combining an LLM and a large image model (LIM). Specifically, the LLM and LIM are used as encoders to perceive multi-modal information under task-specific text prompts, which synergizes multi-source information and task-specific and patient-specific priors for optimized diagnostic performance. The key components of our LIT model and associated techniques are evaluated with an emphasis on 3D lung CT analysis. Our initial results show that the LIT model performs multiple medical tasks well, including lung segmentation, lung nodule detection, and lung cancer classification. Active efforts are in progress to develop large image-language models for superior medical imaging in diverse applications and optimal patient outcomes.

Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review

Cancer has relational information residing at varying scales, modalities, and resolutions of the acquired data, such as radiology, pathology, genomics, proteomics, and clinical records. Integrating diverse data types can improve the accuracy and reliability of cancer diagnosis and treatment. There can be disease-related information that is too subtle for humans or existing technological tools to discern visually. Traditional methods typically focus on partial or unimodal information about biological systems at individual scales and fail to encapsulate the complete spectrum of the heterogeneous nature of data. Deep neural networks have facilitated the development of sophisticated multimodal data fusion approaches that can extract and integrate relevant information from multiple sources. Recent deep learning frameworks such as Graph Neural Networks (GNNs) and Transformers have shown remarkable success in multimodal learning. This review article provides an in-depth analysis of the state-of-the-art in GNNs and Transformers for multimodal data fusion in oncology settings, highlighting notable research studies and their findings. We also discuss the foundations of multimodal learning, inherent challenges, and opportunities for integrative learning in oncology. By examining the current state and potential future developments of multimodal data integration in oncology, we aim to demonstrate the promising role that multimodal neural networks can play in cancer prevention, early detection, and treatment through informed oncology practices in personalized settings.

Graph Neural Networks for Breast Cancer Data Integration

International initiatives such as METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) have collected several multigenomic and clinical data sets to identify the undergoing molecular processes taking place throughout the evolution of various cancers. Numerous Machine Learning and statistical models have been designed and trained to analyze these types of data independently, however, the integration of such differently shaped and sourced information streams has not been extensively studied. To better integrate these data sets and generate meaningful representations that can ultimately be leveraged for cancer detection tasks could lead to giving well-suited treatments to patients. Hence, we propose a novel learning pipeline comprising three steps - the integration of cancer data modalities as graphs, followed by the application of Graph Neural Networks in an unsupervised setting to generate lower-dimensional embeddings from the combined data, and finally feeding the new representations on a cancer sub-type classification model for evaluation. The graph construction algorithms are described in-depth as METABRIC does not store relationships between the patient modalities, with a discussion of their influence over the quality of the generated embeddings. We also present the models used to generate the lower-latent space representations: Graph Neural Networks, Variational Graph Autoencoders and Deep Graph Infomax. In parallel, the pipeline is tested on a synthetic dataset to demonstrate that the characteristics of the underlying data, such as homophily levels, greatly influence the performance of the pipeline, which ranges between 51\% to 98\% accuracy on artificial data, and 13\% and 80\% on METABRIC. This project has the potential to improve cancer data understanding and encourages the transition of regular data sets to graph-shaped data.

Transfer Learning by Cascaded Network to identify and classify lung nodules for cancer detection

Lung cancer is one of the most deadly diseases in the world. Detecting such tumors at an early stage can be a tedious task. Existing deep learning architecture for lung nodule identification used complex architecture with large number of parameters. This study developed a cascaded architecture which can accurately segment and classify the benign or malignant lung nodules on computed tomography (CT) images. The main contribution of this study is to introduce a segmentation network where the first stage trained on a public data set can help to recognize the images which included a nodule from any data set by means of transfer learning. And the segmentation of a nodule improves the second stage to classify the nodules into benign and malignant. The proposed architecture outperformed the conventional methods with an area under curve value of 95.67\%. The experimental results showed that the classification accuracy of 97.96\% of our proposed architecture outperformed other simple and complex architectures in classifying lung nodules for lung cancer detection.