Colorectal cancer (CRC) is the third leading cause of death globally. Early detection and removal precancerous polyps can significantly reduce chance CRC patient death. Currently, polyp rate mainly depends on skill expertise gastroenterologists. Over time, unidentified develop into cancer. Machine learning has recently emerged as a powerful method in assisting clinical diagnosis. Several classification models have been proposed to identify polyps, but their performance not comparable an...

One of the fundamental and crucial tasks for automated diagnosis colorectal cancer is segmentation acute gastrointestinal lesions, most commonly polyps. Therefore, in this work, we present a novel lightweight encoder-decoder mode architecture with attention mechanism to address challenging task.The proposed Li-SegPNet harnesses cross-dimensional interaction feature maps encoder block modified triplet attention. We have used atrous spatial pyramid pooling handle problem segmenting objects at...

Abstract Countless cybercrime instances have shown the need for detecting and blocking obscene material from social media sites. Deep learning methods (DLMs) outperformed in recognizing content flooded on many online platforms. However, these contemporary DLMs primarily treat recognition of as a simple task binary classification, rather than focusing labelling areas. Hence, could not pay attention to fact that misclassification samples are so diverse. Therefore, this paper focuses two...

This paper suggests an automated system for segmentation of the Lumbo-Sacral (LS) Magnetic Resonance Imaging (MRI) spine and evaluation its geometrical characteristics. The LS MRI is segmented into anatomical parts such as vertebrae, intervertebral discs (IVDs), canal vertebral height width, IVDs diameter, index signal intensity parameters are computed to facilitate automatic analysis. To overcome subjectivity variability that come with manual analysis, expert-verified dataset developed....

In this paper, we address a current problem in medical image processing, the detection of colorectal cancer from colonoscopy videos. According to worldwide statistics, is one most common cancers. The process screening and removal pre-cancerous cells large intestine crucial task date. traditional manual dependent on expertise practitioner. two-stage classification proposed detect cancer. first stage, frames video are extracted rated as significant if it contains polyp, these results then...

This proposed work is an attempt to design advance vehicle security system that uses GPS and GSM prevent theft determine the exact location of vehicle. Today happening on parking or in some insecure place. The safety exceptionally essential. designed using technology. contains module, modem, Infrared sensors, DTMF tone decoder, 8051 microcontroller, relay switch, paint spray high voltage mesh. track current vehicle, there are two types tracking used one online other offline tracking. also...

Melanoma is a type of skin cancer that starts in the cells (melanocytes) govern color your skin. most lethal one among all other diseases and only reason for 77% deaths due to cancer. The best way reduce these detect at its early stages so it can be treated cured with minor treatment or surgeries. To speed up improve process detection, we propose an automatic classification method melanoma using advanced deep neural network. Deep learning models require large dataset work efficiently, but...

Cardiotocography or CTG is a technique for monitoring the fetal heart rate and uterine contractions during pregnancy. It used to assess well-being as well identify distress early. The interpretation of can help determine whether pregnancy at high-risk low-risk. An aberrant may necessitate further study and, in some cases, intervention. These forecasts are evaluated real-time clinical judgment support framework which provides useful information that be learn more about state. current...

Introduction: Essential genes are essential for the survival of various species. These a family linked to critical cellular activities species survival. coded proteins that regulate central metabolism, gene translation, deoxyribonucleic acid replication, and fundamental structure facilitate intracellular extracellular transport. preserve crucial genomics information may hold key detailed knowledge life evolution. studies have long been regarded as vital topic in computational biology due...

Prediction of stock market data is difficult because its complex and highly volatile nature. In this work the historical as well technical indicators are implemented for purpose prediction. Different features extracted using CNN technique further prediction performed dropout based LSTM technique. The basic aim study optimization accuracy price. taken input data. sub max layer substituted with KELM (Kernel Based Extreme Learning Machine). This paper shows a hybrid system applied on variety...

Purpose The objective of the proposed work is to identify most commonly occurring non–small cell carcinoma types, such as adenocarcinoma and squamous carcinoma, within human population. Another reduce false positive rate during classification. Design/methodology/approach In this work, a hybrid method using convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) long-short-term memory (LSTMs) has been distinguish between lung carcinoma. To extract features from images,...

Out of all the cancer related death, lung has highest rate. In this work, a classification technique been proposed. The CT images are processed and HOG features extracted from image. then given to SVM classifier classify type. performance method evaluated in terms specificity, sensitivity, precision, false positive rate, accuracy, F1-score. Accuracy is found be 96% for test set. proposed can utilized identify

Colorectal Cancer has become a major cause of death in recent times. To improve the chances survival, detecting early signs and identifying polyps routine examination is necessary. In this pursuit, an automatic computer-aided diagnosis (CAD) system to detect disease onset crucial. Deep Learning at base advances CAD systems, its successes encourage it be used colorectal cancer analysis. Efficient segmentation from colonoscopy images can aid radiologists immensely task identification...

It is generally noticed that increasing the number of convolutional layers in generic image classification procedures proves to be detrimental model performance terms validation accuracy and loss. Apart from vanilla CNNs, we have state-of-the-art (SOTA) architectures such as ResNet50 (and its variants) which show through use skip-connections, higher metrics are attainable deeper architectures. However, most evaluative converge on a log scale go with diminishing gradient metrics' curves....

Abstract The traditional process of disease diagnosis from medical images follows a manual process, which is tedious and arduous. A computer‐aided (CADs) system can work as an assistive tool to improve the process. In this pursuit, article introduces unique architecture LPNet for classifying colon polyps colonoscopy video frames. Colon are abnormal growth cells in wall. Over time, untreated may cause colorectal cancer. Different convolutional neural networks (CNNs) based systems have been...

Abstract The rampant spread of explicit content across social media can leave a damaging mark on our society. Hence, the need to be vigilant in detecting and curtailing sexually cannot overstated. As such, it becomes paramount discern manage material curb its dissemination safeguard digital communities from harmful effects. In this article, we propose unique technique entitled attention‐enabled pooling (ABP) embedded Swin transformer‐based YOLOv3 (ASYv3) for detection obscene areas present...