Diagnostic ability of deep learning in detection of pancreatic tumour

Abstract

Pancreatic cancer is associated with higher mortality rates due to insufficient diagnosis techniques, often diagnosed at an advanced stage when effective treatment is no longer possible. Therefore, automated systems that can detect cancer early are crucial to improve diagnosis and treatment outcomes. In the medical field, several algorithms have been put into use. Valid and interpretable data are essential for effective diagnosis and therapy. There is much room for cutting-edge computer systems to develop. The main objective of this research is to predict pancreatic cancer early using deep learning and metaheuristic techniques. This research aims to create a deep learning and metaheuristic techniques-based system to predict pancreatic cancer early by analyzing medical imaging data, mainly CT scans, and identifying vital features and cancerous growths in the pancreas using Convolutional Neural Network (CNN) and YOLO model-based CNN (YCNN) models. Once diagnosed, the disease cannot be effectively treated, and its progression is unpredictable. That's why there's been a push in recent years to implement fully automated systems that can sense cancer at a prior stage and improve diagnosis and treatment. The paper aims to evaluate the effectiveness of the novel YCNN approach compared to other modern methods in predicting pancreatic cancer. To predict the vital features from the CT scan and the proportion of cancer feasts in the pancreas using the threshold parameters booked as markers. This paper employs a deep learning approach called a Convolutional Neural network (CNN) model to predict pancreatic cancer images. In addition, we use the YOLO model-based CNN (YCNN) to aid in the categorization process. Both biomarkers and CT image dataset is used for testing. The YCNN method was shown to perform well by a cent percent of accuracy compared to other modern techniques in a thorough review of comparative findings.

One-Stage Detection without Segmentation for Multi-Type Coronary Lesions in Angiography Images Using Deep Learning

“…The confusion matrices for YOLOv5x (Predicted) and manual annotations (True) of four types of lesions are shown in Figure 5 ( IoU = 0.1). All the detected regions were taken into account when calculating the confusion matrix’s values, similar to other studies on YOLO [ 32 , 33 , 34 ]. Two angle views of the right coronary showed the same performance.…”

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

“…Deep learning is a specific area within the broader field of machine learning that utilizes artificial neural networks consisting of multiple layers to effectively analyze intricate data and derive sophisticated features. The utilization of convolutional neural networks (CNNs) has been extensively employed in the realm of image analysis, specifically in the identification and partitioning of pancreatic cancer from medical images such as CT scans or MRI [41][42][43][44]. Recurrent neural networks (RNNs) have demonstrated their applicability in the analysis of time-series data, particularly in the context of predicting patient outcomes or monitoring treatment response in pancreatic cancer [42,45,46].…”

“…The utilization of convolutional neural networks (CNNs) has been extensively employed in the realm of image analysis, specifically in the identification and partitioning of pancreatic cancer from medical images such as CT scans or MRI [41][42][43][44]. Recurrent neural networks (RNNs) have demonstrated their applicability in the analysis of time-series data, particularly in the context of predicting patient outcomes or monitoring treatment response in pancreatic cancer [42,45,46]. A trend analysis of AI in pancreatic cancer research is provided in Figure 3.…”

Pancreatic cancer in Saudi Arabia (2005–2020): increasing trend

“…Therefore, screening would lead to early disease detection, improving the possibility for a better prognosis and treatment outcome1 [ 17 – 19 ]. Nevertheless, the disease is associated with high mortality rates due to insufficient screening methodologies that result in late diagnosis of advanced stages that hinder the efforts of effective treatment of the disease [ 20 ].…”