Detection and Classification Method for Early-Stage Colorectal Cancer Using Dyadic Wavelet Packet Transform
Incorporating deep learning into computer-aided medical diagnosis has led to significant advancements. However, a major challenge remains in interpreting deep learning models, especially in identifying the features critical for diagnosis. This study proposes a method for the early detection of color...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10830507/ |
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| _version_ | 1832592946976784384 |
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| author | Daigo Takano Hajime Omura Teruya Minamoto |
| author_facet | Daigo Takano Hajime Omura Teruya Minamoto |
| author_sort | Daigo Takano |
| collection | DOAJ |
| description | Incorporating deep learning into computer-aided medical diagnosis has led to significant advancements. However, a major challenge remains in interpreting deep learning models, especially in identifying the features critical for diagnosis. This study proposes a method for the early detection of colorectal cancer. The method uses the dyadic wavelet packet transform, root mean square, and zero count metrics to extract meaningful features from endoscopic images. These features effectively capture variations in pixel values and delineate contours and patterns. A standout strength of the proposed method lies in its interpretability. The direct feature extraction process enhances both the method’s transparency and the classification results’ comprehensibility. Furthermore, by analyzing the mean absolute values of principal component scores, we provide insightful explanations for misclassification cases, offering a deeper understanding of the classification mechanics. We experimentally evaluated the performance of the proposed method against five deep learning models on three diverse datasets. The results showed that our method achieved a validation accuracy of 99.2% on the Saga University Hospital dataset, 97.4% on the Kvasir-SEG dataset, and 97.8% on the CVC-ClinicDB dataset. These scores surpassed all other models tested, with precision, recall, and F1-measure values consistently higher across datasets, highlighting the method’s robustness and potential for reliable early colorectal cancer detection. |
| format | Article |
| id | doaj-art-ad121bdc9c8047ccbda4814a1c654e05 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ad121bdc9c8047ccbda4814a1c654e052025-01-21T00:01:44ZengIEEEIEEE Access2169-35362025-01-01139276928910.1109/ACCESS.2025.352678610830507Detection and Classification Method for Early-Stage Colorectal Cancer Using Dyadic Wavelet Packet TransformDaigo Takano0Hajime Omura1https://orcid.org/0009-0007-3267-552XTeruya Minamoto2https://orcid.org/0000-0001-6081-2324Graduate School of Science and Engineering, Saga University, Saga, JapanGraduate School of Science and Engineering, Saga University, Saga, JapanGraduate School of Science and Engineering, Saga University, Saga, JapanIncorporating deep learning into computer-aided medical diagnosis has led to significant advancements. However, a major challenge remains in interpreting deep learning models, especially in identifying the features critical for diagnosis. This study proposes a method for the early detection of colorectal cancer. The method uses the dyadic wavelet packet transform, root mean square, and zero count metrics to extract meaningful features from endoscopic images. These features effectively capture variations in pixel values and delineate contours and patterns. A standout strength of the proposed method lies in its interpretability. The direct feature extraction process enhances both the method’s transparency and the classification results’ comprehensibility. Furthermore, by analyzing the mean absolute values of principal component scores, we provide insightful explanations for misclassification cases, offering a deeper understanding of the classification mechanics. We experimentally evaluated the performance of the proposed method against five deep learning models on three diverse datasets. The results showed that our method achieved a validation accuracy of 99.2% on the Saga University Hospital dataset, 97.4% on the Kvasir-SEG dataset, and 97.8% on the CVC-ClinicDB dataset. These scores surpassed all other models tested, with precision, recall, and F1-measure values consistently higher across datasets, highlighting the method’s robustness and potential for reliable early colorectal cancer detection.https://ieeexplore.ieee.org/document/10830507/Colorectal cancer detectioncomputer-aided diagnosisdyadic wavelet packet transformendoscopic imaginginterpretabilitySHAP |
| spellingShingle | Daigo Takano Hajime Omura Teruya Minamoto Detection and Classification Method for Early-Stage Colorectal Cancer Using Dyadic Wavelet Packet Transform IEEE Access Colorectal cancer detection computer-aided diagnosis dyadic wavelet packet transform endoscopic imaging interpretability SHAP |
| title | Detection and Classification Method for Early-Stage Colorectal Cancer Using Dyadic Wavelet Packet Transform |
| title_full | Detection and Classification Method for Early-Stage Colorectal Cancer Using Dyadic Wavelet Packet Transform |
| title_fullStr | Detection and Classification Method for Early-Stage Colorectal Cancer Using Dyadic Wavelet Packet Transform |
| title_full_unstemmed | Detection and Classification Method for Early-Stage Colorectal Cancer Using Dyadic Wavelet Packet Transform |
| title_short | Detection and Classification Method for Early-Stage Colorectal Cancer Using Dyadic Wavelet Packet Transform |
| title_sort | detection and classification method for early stage colorectal cancer using dyadic wavelet packet transform |
| topic | Colorectal cancer detection computer-aided diagnosis dyadic wavelet packet transform endoscopic imaging interpretability SHAP |
| url | https://ieeexplore.ieee.org/document/10830507/ |
| work_keys_str_mv | AT daigotakano detectionandclassificationmethodforearlystagecolorectalcancerusingdyadicwaveletpackettransform AT hajimeomura detectionandclassificationmethodforearlystagecolorectalcancerusingdyadicwaveletpackettransform AT teruyaminamoto detectionandclassificationmethodforearlystagecolorectalcancerusingdyadicwaveletpackettransform |