6 research outputs found
Improving ductal carcinoma in situ classification by convolutional neural network with exponential linear unit and rank-based weighted pooling
Ductal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal
therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and
although it has high accuracy (~88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial
intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence
based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240
DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and
rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were
chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08±1.22%, a specificity
of 93.58±1.49 and an accuracy of 93.83±0.96. The proposed method gives superior performance than eight state-of-theart
approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve
diagnostic accuracy.British Heart Foundation Accelerator Award, UKRoyal Society International Exchanges Cost Share Award, UK
RP202G0230Hope Foundation for Cancer Research, UK
RM60G0680Medical Research Council Confidence in Concept Award, UK
MC_PC_17171MINECO/FEDER, Spain/Europe
RTI2018-098913-B100
A-TIC-080-UGR1