3 research outputs found
Classification of Colorectal Cancer Polyps via Transfer Learning and Vision-Based Tactile Sensing
In this study, to address the current high earlydetection miss rate of
colorectal cancer (CRC) polyps, we explore the potentials of utilizing transfer
learning and machine learning (ML) classifiers to precisely and sensitively
classify the type of CRC polyps. Instead of using the common colonoscopic
images, we applied three different ML algorithms on the 3D textural image
outputs of a unique vision-based surface tactile sensor (VS-TS). To collect
realistic textural images of CRC polyps for training the utilized ML
classifiers and evaluating their performance, we first designed and additively
manufactured 48 types of realistic polyp phantoms with different hardness,
type, and textures. Next, the performance of the used three ML algorithms in
classifying the type of fabricated polyps was quantitatively evaluated using
various statistical metrics.Comment: Accepted to IEEE Sensors 2022 Conferenc
Towards Reliable Colorectal Cancer Polyps Classification via Vision Based Tactile Sensing and Confidence-Calibrated Neural Networks
In this study, toward addressing the over-confident outputs of existing
artificial intelligence-based colorectal cancer (CRC) polyp classification
techniques, we propose a confidence-calibrated residual neural network.
Utilizing a novel vision-based tactile sensing (VS-TS) system and unique CRC
polyp phantoms, we demonstrate that traditional metrics such as accuracy and
precision are not sufficient to encapsulate model performance for handling a
sensitive CRC polyp diagnosis. To this end, we develop a residual neural
network classifier and address its over-confident outputs for CRC polyps
classification via the post-processing method of temperature scaling. To
evaluate the proposed method, we introduce noise and blur to the obtained
textural images of the VS-TS and test the model's reliability for non-ideal
inputs through reliability diagrams and other statistical metrics
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Towards an Intelligent Colorectal Cancer Polyp Classification and Detection System Using Vision-Based Surface Tactile Sensing
Colorectal cancer (CRC) is the second leading cause of cancer-related deaths and is projected to affect 3.2 million individuals by 2040. However, detecting the cancer at an early stage can significantly improve the chances of successful treatment. CRC polyp miss rates are as high as 27% for serrated polyps and 34% for flat polyps, particularly because the morphological characteristics of polyps are patient-dependent. There is a need for sensing devices and computer aided diagnosis tools to assist clinicians in improving the detection and classification of CRC polyps. To address these limitations and mitigate the risks associated with CRC, we have developed a novel approach utilizing a vision-based surface tactile sensor (VS-TS) and a complementary artificial intelligence (AI) algorithms. The research presented in this report is published in [1, 2, 3] and under review at International Conference on Robotics and Automation (ICRA) 2023.
In this study, we present the various iterations of our sensors and algorithms that led to the current version of our system. We designed realistic polyp phantoms representing the textures of the Paris (48 polyps) and Kudo (160 polyps) classification systems with varying stiffnesses to evaluate our system. Initially, using the first sensor iteration, we developed a support vector machine texture classification algorithm using histogram of oriented gradients as feature representation and t-Distributed Stochastic Neighbor Embedding algorithm for stiffness/material differentiation. In the next stage, we utilized a more advanced, pre-trained ResNet-18 architecture. We also developed the HySenSe, a hyper sensitive and high fidelity sensor, to minimize the applied forces and capture more detailed textures. With this sensor, we designed a computationally efficient and high-performing deep learning model utilizing dilated residual networks to classify pit-pattern or Kudo polyp textures. Finally, we developed a real-time polyp detection using YOLO v5 and the pit-pattern classification system.
The initial SVM model achieved an accuracy of 93.75%, and we were able to isolate the hardest polyp material (M3) from softer, more similar materials (M1, M2). Utilizing the 1 ResNet-18 model, we achieved an accuracy of 92.88%. Lastly, the proposed model utilizing dilated residual networks model classified various pit-pattern textures with a 94% accuracy using a dataset of 160 unique polyps. 1.4
The results demonstrate the potential of our proposed approach as a promising diagnostic tool for the early detection and diagnosis of colorectal cancer. Our system has the potential to have a significant impact on the prevention and treatment of this disease. In the future, we aim to focus on training the models on excised human polyps and evaluating the performance of this system in a pre-clinical setting.Biomedical Engineerin