Design a deep learning model -- 2
Budget: $30 – $250 USD
Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are frequent reported cases of non-small cell lung cancer (NSCLC),
responsible for a large fraction of cancer deaths worldwide. In this study, we aim to investigate the potential of NSCLC histology
classification into AC and SCC by applying different feature extraction and classification techniques on pre-treatment CT images.
The employed image dataset (102 patients) was taken from the publicly available cancer imaging archive collection (TCIA). We
investigated four different families of techniques: (a) radiomics with two classifiers (kNN and SVM), (b) four state-of-the-art
convolutional neural networks (CNNs) with transfer learning and fine tuning (Alexnet, ResNet101, Inceptionv3 and
InceptionResnetv2), (c) a CNN combined with a long short-term memory (LSTM) network to fuse information about the spatial
coherency of tumor’s CT slices, and (d) combinatorial models (LSTM + CNN + radiomics). In addition, the CT images were
independently evaluated by two expert radiologists. Our results showed that the best CNN was Inception (accuracy = 0.67, auc =
0.74). LSTM + Inception yielded superior performance than all other methods (accuracy = 0.74, auc = 0.78). Moreover, LSTM +
Inception outperformed experts by 7–25% (p < 0.05). The proposed methodology does not require detailed segmentation of the
tumor region and it may be used in conjunction with radiological findings to improve clinical decision-making.
responsible for a large fraction of cancer deaths worldwide. In this study, we aim to investigate the potential of NSCLC histology
classification into AC and SCC by applying different feature extraction and classification techniques on pre-treatment CT images.
The employed image dataset (102 patients) was taken from the publicly available cancer imaging archive collection (TCIA). We
investigated four different families of techniques: (a) radiomics with two classifiers (kNN and SVM), (b) four state-of-the-art
convolutional neural networks (CNNs) with transfer learning and fine tuning (Alexnet, ResNet101, Inceptionv3 and
InceptionResnetv2), (c) a CNN combined with a long short-term memory (LSTM) network to fuse information about the spatial
coherency of tumor’s CT slices, and (d) combinatorial models (LSTM + CNN + radiomics). In addition, the CT images were
independently evaluated by two expert radiologists. Our results showed that the best CNN was Inception (accuracy = 0.67, auc =
0.74). LSTM + Inception yielded superior performance than all other methods (accuracy = 0.74, auc = 0.78). Moreover, LSTM +
Inception outperformed experts by 7–25% (p < 0.05). The proposed methodology does not require detailed segmentation of the
tumor region and it may be used in conjunction with radiological findings to improve clinical decision-making.