Arukonda, Srinivas
(VIT Bhopal University, School of Computing Science and Engineering, Sehore, MP)
,
Sountharrajan, S.
(VIT Bhopal University, School of Computing Science and Engineering, Sehore, MP)
Lung cancer is one of the most prevalent cancer-related diseases with a high mortality rate, and this is largely due to the lateness in detecting the presence of malignancy. Again, the conventional methods used in the diagnosis of lung cancer have had their shortfalls. While the effectiveness of com...
Lung cancer is one of the most prevalent cancer-related diseases with a high mortality rate, and this is largely due to the lateness in detecting the presence of malignancy. Again, the conventional methods used in the diagnosis of lung cancer have had their shortfalls. While the effectiveness of computerized tomography in detecting this malignancy, the large volumes of data that radiologists have to process not only present an arduous task but may also slow down the process of detecting lung cancer early enough for treatment to take its course. It is against this backdrop that computer-aided diagnostic (CAD) systems have been designed. One of such is the convolutional neural network, a method that best describes a group of deep learning models featuring filters that can be trained with local pooling operations being incorporated on input CT images in an alternating manner to create an array of hierarchical complex features. The need to have this type of data-driven technique is further informed by the attempt to ensure successful segmentation of lung nodules, a step that cannot be overruled when striving for a good model of detection or diagnosis. There are variations and models of the convolutional neural networks that have been effectively put to use in the lung nodule detection. The 2D CNN model has been utilized in the medical field for quite a while now, and as it has displayed its many strengths, so could the limitations not be hidden. It is in addressing these limitations and improving on the detection prowess of the convolutional neural network that the 3D model is now fast gaining traction. The 3D models have been reported to return pronounced sensitivity and specificity in detection of lung nodules, but the issues of time-consumption, training complexities and hardware memory usage could make it difficult to implement the 3D model in the medical field. In this paper, review the advances that have been made in the area of adopting 3D CNN model in the diagnosis of lung cancer.
Lung cancer is one of the most prevalent cancer-related diseases with a high mortality rate, and this is largely due to the lateness in detecting the presence of malignancy. Again, the conventional methods used in the diagnosis of lung cancer have had their shortfalls. While the effectiveness of computerized tomography in detecting this malignancy, the large volumes of data that radiologists have to process not only present an arduous task but may also slow down the process of detecting lung cancer early enough for treatment to take its course. It is against this backdrop that computer-aided diagnostic (CAD) systems have been designed. One of such is the convolutional neural network, a method that best describes a group of deep learning models featuring filters that can be trained with local pooling operations being incorporated on input CT images in an alternating manner to create an array of hierarchical complex features. The need to have this type of data-driven technique is further informed by the attempt to ensure successful segmentation of lung nodules, a step that cannot be overruled when striving for a good model of detection or diagnosis. There are variations and models of the convolutional neural networks that have been effectively put to use in the lung nodule detection. The 2D CNN model has been utilized in the medical field for quite a while now, and as it has displayed its many strengths, so could the limitations not be hidden. It is in addressing these limitations and improving on the detection prowess of the convolutional neural network that the 3D model is now fast gaining traction. The 3D models have been reported to return pronounced sensitivity and specificity in detection of lung nodules, but the issues of time-consumption, training complexities and hardware memory usage could make it difficult to implement the 3D model in the medical field. In this paper, review the advances that have been made in the area of adopting 3D CNN model in the diagnosis of lung cancer.
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