Brain tumor localization and segmentation from MRI is a challenging task in the field of Medical AI. With recent advancements, various techniques have been developed to assist medical professionals in detecting brain tumors using Artificial Intelligence. Although machine learning algorithms have shown efficiency in tumor segmentation, they often lack interpretability, making it difficult to trust and validate their predictions. In this paper, we developed an interpre table UNet model for brain tumor segmentation, incorporating Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP) to enhance model transparency. The BraTS2020 benchmark dataset was used for training and evaluation. The model demonstrated strong performance, achieving an accuracy of 99.35%. Grad-CAM was applied to visualize the regions of interest identified by the model, enhancing interpretability. Additionally, the SHAP library was utilized to explain the predictions of multiple machine learning models (Random Forest, KNN, SVC, MLP) employed for estimating patient survival days, further contributing to the model’s transparency and clinical utility.

Biomedical named entity recognition (BNER) is a critical task for biomedical information extraction. Most popular BNER approaches based on deep learning utilize words and characters as features to represent medical texts. However, many medical terminologies are composed of multiple words and characters, and splitting medical terminology into multiple words (or characters) and assigning weight values for each word (or character) by a standard attention mechanism may disperse the attention score and result in a lower weight value for the medical terminology. This paper proposes a Dictionary-guided Attention Network (DGAN) for BNER in Chinese electronic medical records (EMRs). First, the medical concepts are extracted as large-size words to supplement the comprehensive semantic information of the medical terminology by matching the EMR text to the biomedical dictionary. Then, based on the matched dictionary results, an optimized attention strategy is proposed to focus on the medical concept and adaptively assign higher weights to the characters contained in a concept. Furthermore, semisupervised learning is introduced to reduce the manual labeling of data and to handle the entities not defined in the medical dictionary. To validate our new model in recognizing biomedical named entities, we conduct comprehensive experiments on a real-world Chinese EMR dataset and the CCKS2017 dataset. Our promising results illustrate that our method not only achieves a state-of-the-art performance in BNER but also reduces manual data annotation.

Background: Classification of infants has always been considered a crucial task in the literature related to predicting small for gestational age (SGA) infants. Traditional medical guidance becomes increasingly unsatisfactory, as patients' care should be centered not only on clinical symptoms but also on socio-economic and demographic factors. Infants with excessive gestational weight exhibit serious maternal complications that require early intervention to stream-line the progression of the disease. Methods: This research proposes to use the Stacked Generalization Scheme (SGS) to predict Small for Gestational (SGA) Infants on the dataset collected from the National Pre-Pregnancy and Examination Program of China. A Cleaned Feature Vector (CFV) is created that entertains issues related to missing values, discretization of fields, and data imbalance. Later, Knowledge-Driven Data (KDD) Features are extracted from the obtained CFV, and the proposed scheme is applied to predict SGA infants. The proposed scheme superposed the existing baseline approaches by achieving the highest precision, recall, and AUC scores of 0.94, 0.85, and 0.89, respectively. Conclusion: The proposed SGS can predict SGA infants accurately compared to existing baseline schemes using KDD parameters, which can help pediatricians develop an efficient SGA Prognosis process.

The medical record system is becoming more and more irreplaceable in the medical industry, and the electronic medical record data continues to grow over time. There is a lot of knowledge and information in these accumulated medical resources that can be used for medical services, but how to obtain this valuable medical information is a difficult problem that needs to be overcome. Event extraction belongs to information extraction technology, which is an effective solution that can automatically mine knowledge and information from text data. Many studies have applied it to the text data of electronic medical records to extract medical events related to medical treatment. They have achieved certain results for medical services. However, these studies usually lack the synergistic consideration of global features and local features of medical text information in terms of Chinese medical record text mining and utilization. To better solve this problem, we try to propose a hybrid neural network model (BCBC) based on CNN-BILSTM-CRF. By integrating CNN and BILSTM, the local and global features of the text are comprehensively extracted, which makes up for the insufficient semantic capture of a single model in the traditional method. Through experimental verification, the hybrid neural network model BCBC proposed in this paper outperforms other previous advanced methods in event extraction and can efficiently complete the event extraction task.

Several developments in computational image processing methods assist the radiologist in detecting abnormal breast tissue in recent years. Consequently, deep learning-based models have become crucial for early screening and interpretation of mammographic images for breast masses diagnosis, helping for successful treatment. Breast masses and calcification is an essential parameter for the prognosis of breast cancer. However, the mammographic image’s mass detection needs a deeper investigation due to the breast masses’ heterogeneity and anomalies’ characteristics that are easily confused with other objects present in the image. Hence, this study proposed a deep learning-based convolutional neural network (ConvNet) that will incorporate both mammography and clinical variables to predict and classify breast masses to assist the expert’s decision-making processes. We trained our proposed model with 322 scanned digital mammographic images of the MIAS (Mammogram Image Analysis Society) dataset and 580 images of the private dataset to evaluate the performance, which is highly imbalanced. This study aimed to perform an automatic and comprehensive characterization of breast masses using appropriate layers deep ConvNet model with high accuracy true-positive rate, decreased error rate and applying data-augmentation techniques. We obtained a classification accuracy of 97% applying the filtered deep features, which is the best performance from the existing approaches.

Network forensics can be an expansion associated with network security design which typically emphasizes avoidance and detection of community assaults. It covers the necessity for dedicated investigative abilities. When you look at the design, this indeed currently allows investigating harmful behavior in communities. It will help organizations to examine external and community this is undoubtedly around. It is also important for police force investigations. Network forensic techniques can be used to identify the source of the intrusion and the intruder’s location. Forensics can resolve many cybercrime cases using the methods of network forensics. These methods can extract intruder’s information, the nature of the intrusion, and how it can be prevented in the future. These techniques can also be used to avoid attacks in near future. Modern network forensic techniques face several challenges that must be resolved to improve the forensic methods. Some of the key challenges include high storage speed, the requirement of ample storage space, data integrity, data privacy, access to IP address, and location of data extraction. The details concerning these challenges are provided with potential solutions to these challenges. In general, the network forensic tools and techniques cannot be improved without addressing these challenges of the forensic network. This paper proposed a thematic taxonomy of classifications of network forensic techniques based on extensive. The classification has been carried out based on the target datasets and implementation techniques while performing forensic investigations. For this purpose, qualitative methods have been used to develop thematic taxonomy. The distinct objectives of this study include accessibility to the network infrastructure and artifacts and collection of evidence against the intruder using network forensic techniques to communicate the information related to network attacks with minimum false-negative results. It will help organizations to investigate external and internal causes of network security attacks.

Large for gestational (LGA) means the fetus having an abnormal birth weight. It adheres severe complications during and after the maternal period. Therefore, this research presents an ensemble classification scheme using Chinese National Pre-Pregnancy Examination Program dataset to classify a fetus as an LGA or non-LGA based on provided Chinese LGA classification guidelines. Moreover, the proposed scheme is comprised of data cleansing and ensemble classification schemes that have drastically improved the LGA classification process with improved performance results compared to present published studies. Therefore, the recommended scheme can be utilized by healthcare professionals to build an enhanced and reliable LGA classification system.

Cataract is one of the most common causes of visual blindness, about 90% of the elderly over 60 years old with visual impairment in China have cataract diseases, and about 90% of eye diseases are diagnosed by observing the fundus. The observation of fundus has always been a necessary mean of diagnosing a cataract. Moreover, it is highly uncertain about judging the degree of lesions based on experience, but also the efficiency of this method is very low. Therefore, employing a computer-aided diagnostic system to perform the automatic grading of cataract is of great research value of practical use. Most of the studies reported in the literature utilize histogram equalization (Histeq) or other image enhancement methods based on gray value changes to improve the contrast. In this paper, the adaptive window model (AWM) is used to enhance the contrast between the vessel and the background. We used features extracted from the spoke features of the image for cataract grading. The best average accuracy achieved by Support Vector Machine (Back Propagation Neural Network) is 80.12% (78.26%) when AWM is used to enhance the contrast. Furthermore, it is even higher than 73.29% (75.16%) when the Histeq is used as an image enhancement technique.

From centuries, information security has been an attractive topic for security officials, intruders, hackers and other communication sectors throughout the world. Cryptography and steganography are widely practiced for secure communication over the internet. In steganography, data hiding capacity has been a great challenge for the research community and security officials. In this research, a novel algorithm is elaborated to conceal secret data with higher cover text capacity by using three different unicode characters such as zero width joiner, zero width non-joiner and zero width character. English text is taken as a message carrier. Before embedding a secret message into cover text, one's complement is applied on binary value of specific characters in secret message. Furthermore, 'Steger' is developed for the practical implementation of designed algorithm. The results revealed that newly designed algorithm reported higher data hiding capacity with security and size efficiency. This is an astonishing increase in data hiding capacity of carrier text. The unicode approach was efficiently and effectively used to reduce the attention of intruders.