Al Husaini, Mohammed Abdulla Salim and Habaebi, Mohamed Hadi and Gunawan, Teddy Surya and Islam, Md. Rafiqul and Elsheikh, Elfatih A. A. and Suliman, F.M.
(2021)
Thermal-based early breast cancer detection using inception V3,
inception V4 and modified inception MV4.
Neural Computing and Applications, Early Access (online).
pp. 1-16.
ISSN 0941-0643
E-ISSN 1433-3058
Abstract
Breast cancer is one of the most significant causes of death for women around the world. Breast thermography supported by
deep convolutional neural networks is expected to contribute significantly to early detection and facilitate treatment at an
early stage. The goal of this study is to investigate the behavior of different recent deep learning methods for identifying
breast disorders. To evaluate our proposal, we built classifiers based on deep convolutional neural networks modelling
inception V3, inception V4, and a modified version of the latter called inception MV4. MV4 was introduced to maintain the
computational cost across all layers by making the resultant number of features and the number of pixel positions equal.
DMR database was used for these deep learning models in classifying thermal images of healthy and sick patients. A set of
epochs 3–30 were used in conjunction with learning rates 1 9 10–3, 1 9 10–4 and 1 9 10–5, Minibatch 10 and different
optimization methods. The training results showed that inception V4 and MV4 with color images, a learning rate of
1 9 10–4, and SGDM optimization method, reached very high accuracy, verified through several experimental repetitions.
With grayscale images, inception V3 outperforms V4 and MV4 by a considerable accuracy margin, for any optimization
methods. In fact, the inception V3 (grayscale) performance is almost comparable to inception V4 and MV4 (color)
performance but only after 20–30 epochs. inception MV4 achieved 7% faster classification response time compared to V4.
The use of MV4 model is found to contribute to saving energy consumed and fluidity in arithmetic operations for the
graphic processor. The results also indicate that increasing the number of layers may not necessarily be useful in improving
the performance.
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