The Use of Hybrid CNN-RNN Deep Learning Models to Discriminate Tumor Tissue in Dynamic Breast Thermography
Breast cancer is one of the leading causes of death for women worldwide, and early detection can help reduce the death rate. Infrared thermography has gained popularity as a non-invasive and rapid method for detecting this pathology and can be further enhanced by applying neural networks to extract...
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MDPI AG
2024-12-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/10/12/329 |
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| author | Andrés Munguía-Siu Irene Vergara Juan Horacio Espinoza-Rodríguez |
| author_facet | Andrés Munguía-Siu Irene Vergara Juan Horacio Espinoza-Rodríguez |
| author_sort | Andrés Munguía-Siu |
| collection | DOAJ |
| description | Breast cancer is one of the leading causes of death for women worldwide, and early detection can help reduce the death rate. Infrared thermography has gained popularity as a non-invasive and rapid method for detecting this pathology and can be further enhanced by applying neural networks to extract spatial and even temporal data derived from breast thermographic images if they are acquired sequentially. In this study, we evaluated hybrid convolutional-recurrent neural network (CNN-RNN) models based on five state-of-the-art pre-trained CNN architectures coupled with three RNNs to discern tumor abnormalities in dynamic breast thermographic images. The hybrid architecture that achieved the best performance for detecting breast cancer was VGG16-LSTM, which showed accuracy (ACC), sensitivity (SENS), and specificity (SPEC) of 95.72%, 92.76%, and 98.68%, respectively, with a CPU runtime of 3.9 s. However, the hybrid architecture that showed the fastest CPU runtime was AlexNet-RNN with 0.61 s, although with lower performance (ACC: 80.59%, SENS: 68.52%, SPEC: 92.76%), but still superior to AlexNet (ACC: 69.41%, SENS: 52.63%, SPEC: 86.18%) with 0.44 s. Our findings show that hybrid CNN-RNN models outperform stand-alone CNN models, indicating that temporal data recovery from dynamic breast thermographs is possible without significantly compromising classifier runtime. |
| format | Article |
| id | doaj-art-2e58a4842e2c4124a16cd4f1f8d263b8 |
| institution | DOAJ |
| issn | 2313-433X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-2e58a4842e2c4124a16cd4f1f8d263b82025-08-20T02:53:37ZengMDPI AGJournal of Imaging2313-433X2024-12-01101232910.3390/jimaging10120329The Use of Hybrid CNN-RNN Deep Learning Models to Discriminate Tumor Tissue in Dynamic Breast ThermographyAndrés Munguía-Siu0Irene Vergara1Juan Horacio Espinoza-Rodríguez2Department of Computing, Electronics and Mechatronics, Universidad de las Américas Puebla, Sta. Catarina Martir, San Andrés Cholula 72810, MexicoDepartment of Immunology, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Mexico City 04510, MexicoDepartment of Computing, Electronics and Mechatronics, Universidad de las Américas Puebla, Sta. Catarina Martir, San Andrés Cholula 72810, MexicoBreast cancer is one of the leading causes of death for women worldwide, and early detection can help reduce the death rate. Infrared thermography has gained popularity as a non-invasive and rapid method for detecting this pathology and can be further enhanced by applying neural networks to extract spatial and even temporal data derived from breast thermographic images if they are acquired sequentially. In this study, we evaluated hybrid convolutional-recurrent neural network (CNN-RNN) models based on five state-of-the-art pre-trained CNN architectures coupled with three RNNs to discern tumor abnormalities in dynamic breast thermographic images. The hybrid architecture that achieved the best performance for detecting breast cancer was VGG16-LSTM, which showed accuracy (ACC), sensitivity (SENS), and specificity (SPEC) of 95.72%, 92.76%, and 98.68%, respectively, with a CPU runtime of 3.9 s. However, the hybrid architecture that showed the fastest CPU runtime was AlexNet-RNN with 0.61 s, although with lower performance (ACC: 80.59%, SENS: 68.52%, SPEC: 92.76%), but still superior to AlexNet (ACC: 69.41%, SENS: 52.63%, SPEC: 86.18%) with 0.44 s. Our findings show that hybrid CNN-RNN models outperform stand-alone CNN models, indicating that temporal data recovery from dynamic breast thermographs is possible without significantly compromising classifier runtime.https://www.mdpi.com/2313-433X/10/12/329breast cancerdeep learningneural networksCNN-RNNthermographybinary classification |
| spellingShingle | Andrés Munguía-Siu Irene Vergara Juan Horacio Espinoza-Rodríguez The Use of Hybrid CNN-RNN Deep Learning Models to Discriminate Tumor Tissue in Dynamic Breast Thermography Journal of Imaging breast cancer deep learning neural networks CNN-RNN thermography binary classification |
| title | The Use of Hybrid CNN-RNN Deep Learning Models to Discriminate Tumor Tissue in Dynamic Breast Thermography |
| title_full | The Use of Hybrid CNN-RNN Deep Learning Models to Discriminate Tumor Tissue in Dynamic Breast Thermography |
| title_fullStr | The Use of Hybrid CNN-RNN Deep Learning Models to Discriminate Tumor Tissue in Dynamic Breast Thermography |
| title_full_unstemmed | The Use of Hybrid CNN-RNN Deep Learning Models to Discriminate Tumor Tissue in Dynamic Breast Thermography |
| title_short | The Use of Hybrid CNN-RNN Deep Learning Models to Discriminate Tumor Tissue in Dynamic Breast Thermography |
| title_sort | use of hybrid cnn rnn deep learning models to discriminate tumor tissue in dynamic breast thermography |
| topic | breast cancer deep learning neural networks CNN-RNN thermography binary classification |
| url | https://www.mdpi.com/2313-433X/10/12/329 |
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