IEEE BASE PAPER TITLE:
Pancreatic Cancer Classification using Deep Learning
OUR PROPOSED PROJECT TITLE:
Pancreatic Cancer Detection and Classification using Machine Learning and Deep Learning
IEEE BASE PAPER ABSTRACT:
The great majority of the computer systems that are now being utilized for research on medical health systems are based on the most recent technical breakthroughs. Because of the prevalence of pancreatic cancer, a significant number of novel approaches and techniques have emerged in the field of medicine. There are several various classifications that may be applied to the pancreatic cancer that can be found. Utilization of the deep learning technology is going to be the means by which the classification of pancreatic cancer is going to be completed. The classification of pancreatic cancer may be tackled from a variety of angles, each of which can be accomplished via using either technology for machine learning or technology for deep learning. In the past, a diagnosis of pancreatic cancer could be made by using methods such as the Support Vector Machine (SVM), Artificial Neural Networks, Convolution Neural Networks (CNN), and Twin Support Vector Machines. However, these methods are no longer effective (TWSVM). However, these strategies do not deliver an accurate performance. As a result, this study has implemented an Advanced Convolution Neural Networks (ACNN), which are examples of the type of technology known as deep learning. In the vast majority of the existing research works, the classification has been determined by analyzing the images of the patient, which are not always accurately classified; in contrast, the classification in this one is determined by looking at the genetic data of the patient. An accurate number can be obtained by using the blood and urine samples collected from patients since these samples were utilized to construct the genetic data. With the help of constant values and ACNN strategies, the performance rate was enhanced in contrast to the approaches that were currently being used.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
Random Forest Classifier, Naïve Bayes and CNN Model Architecture.
OUR PROPOSED ABSTRACT:
Pancreatic cancer is a highly lethal disease that demands early detection and accurate classification for improved patient outcomes. This project leverages the power of Python-based Machine Learning and Deep Learning techniques to address this critical medical challenge.
In the Machine Learning phase, two powerful algorithms, Random Forest Classifier and Naive Bayes, were employed. The Random Forest Classifier achieved an impressive Accuracy Train score of 100% and a remarkable test score of 99.2%. Similarly, Naive Bayes demonstrated robust performance with an Accuracy Train score of 99.3% and a test score of 99.2%. The dataset utilized in this phase is “Urinary biomarkers for pancreatic cancer,” comprising 590 records with three distinct classes: Control, Benign, and PDAC. Key features of this dataset include creatinine, LYVE1, REG1B, and TFF1. Creatinine serves as an indicator of kidney function, LYVE1’s potential role in tumor metastasis is explored, REG1B’s association with pancreas regeneration is investigated, and TFF1’s relevance to urinary tract repair is studied.
The Deep Learning component of this project utilized Convolutional Neural Network (CNN) architecture. The CNN model exhibited excellent results with a Training accuracy of 98.7% and a Validation accuracy of 100%. The dataset in this phase comprised 1411 images categorized into two classes: normal and pancreatic tumor. This deep learning approach contributes to the project’s robustness and complements the machine learning results.
The combination of Machine Learning and Deep Learning techniques with a focus on urinary biomarkers and imaging analysis provides a comprehensive solution for the detection and classification of pancreatic cancer. This project demonstrates exceptional accuracy, setting the stage for potentially transformative applications in the field of medical diagnosis and early cancer detection.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED.
- Input Devices : Keyboard, Mouse.
- Ram : 8 GB.
- Operating system : Windows 10 Pro.
- Coding Language : Python 3.10.9.
- Web Framework : FLASK.
Naga Vardhani; Gottam Gayathri; Kolusu Leela; Tummala Bhavya; Yalamandala Divya Sravani, “Pancreatic Cancer Classification using Deep Learning”, 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), IEEE Conference, 2023.