1. Home
  2. Vol 5, No 3 (2024)
  3. Irianto

Early Identification of Skin Cancer Using Region Growing Technique and a Deep Learning Algorithm

Suhendro Y. Irianto, Ryan Yunandar, M. S. Hasibuan, Deshinta Arrova Dewi, Nualyai Pitsachart

Abstract


Skin cancer, comprising both melanoma and non-melanoma forms, is a significant public health concern, constituting approximately 5.9% to 7.8% of annual cancer diagnoses. In Indonesia, the predominant form of the disease is basal cell carcinoma (65.5%), followed by squamous cell carcinoma (23%), and malignant melanoma (7.9%). Several studies have shown that early detection of its melanoma form is essential due to the heightened mortality risk. Therefore, this study aimed to assess the efficacy of the Region Growing + LSTM algorithm in improving detection accuracy compared to LSTM. The novelty of the study lay in addressing the inefficiencies of manual dermoscopy image examination and introducing a novel combination of Region Growing segmentation and Deep Learning LSTM for enhanced detection precision. The results showed that the proposed model could identify segmented areas before classification and achieved 96.62% accuracy, outperforming LSTM's 84%. However, LSTM exhibited shorter training and prediction times (39.3 seconds and 3.2 seconds, respectively) compared to Region Growing + LSTM (17 minutes and 2 seconds for training, 3 minutes and 49 seconds for prediction). Although Region Growing + LSTM offered superior accuracy, it required more time than LSTM, showing potential trade-offs between accuracy and efficiency in skin cancer image detection.

 

Doi: 10.28991/HIJ-2024-05-03-07

Full Text: PDF


Keywords


LSTM, Region Growing; Corn Leaf Disease; Public Health; Health Risk.

References


Wang, H., Shen, Z., Zhang, Z., Xu, Z., Li, S., Jiao, S., & Lei, Y. (2021). Improvement of region-merging image segmentation accuracy using multiple merging criteria. Remote Sensing, 13(14), 2782. doi:10.3390/rs13142782.

Dai, A., & Kim, S. J. (2024). Systemic calcineurin inhibitors tacrolimus and voclosporin: A review of off-label dermatologic uses. Journal of the American Academy of Dermatology, 90(2), 358–367. doi:10.1016/j.jaad.2023.05.074.

Dildar, M., Akram, S., Irfan, M., Khan, H. U., Ramzan, M., Mahmood, A. R., Alsaiari, S. A., Saeed, A. H. M., Alraddadi, M. O., & Mahnashi, M. H. (2021). Skin cancer detection: A review using deep learning techniques. International Journal of Environmental Research and Public Health, 18(10), 5479. doi:10.3390/ijerph18105479.

Hamilton, N. A., Pantelic, R. S., Hanson, K., & Teasdale, R. D. (2007). Fast automated cell phenotype image classification. BMC Bioinformatics, 8. doi:10.1186/1471-2105-8-110.

Leiter, U., Keim, U., & Garbe, C. (2020). Epidemiology of skin cancer: Update 2019. In Advances in Experimental Medicine and Biology, 1268, 123–139. doi:10.1007/978-3-030-46227-7_6.

Stevenson, M. L., Glazer, A. M., Cohen, D. E., Rigel, D. S., & Rieder, E. A. (2017). Frequency of total body skin examinations among US dermatologists. Journal of the American Academy of Dermatology, 76(2), 343–344. doi:10.1016/j.jaad.2016.09.017.

Blackledge, J. M. (2005). Digital Image Processing: Mathematical and Computational Methods. In Digital Image Processing: Mathematical and Computational Methods, 1–797. doi:10.1533/9780857099464.

Imtiaz, I., Ahmed, I., Jeon, G., & Muramatsu, S. (2021). An Efficient Image Processing and Machine Learning based Technique for Skin Lesion Segmentation and Classification. 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings, December, 1499–1505.

Arnal Barbedo, J. G. (2013). Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus, 2(1), 660. doi:10.1186/2193-1801-2-660.

Rajendran, V. A., & Shanmugam, S. (2024). Automated Skin Cancer Detection and Classification using Cat Swarm Optimization with a Deep Learning Model. Engineering, Technology and Applied Science Research, 14(1), 12734–12739. doi:10.48084/etasr.6681.

Kuran, U., & Kuran, E. C. (2021). Parameter selection for CLAHE using multi-objective cuckoo search algorithm for image contrast enhancement. Intelligent Systems with Applications, 12. doi:10.1016/j.iswa.2021.200051.

Tang, X., & Rashid Sheykhahmad, F. (2024). Boosted dipper throated optimization algorithm-based Xception neural network for skin cancer diagnosis: An optimal approach. Heliyon, 10(5), e26415. doi:10.1016/j.heliyon.2024.e26415.

Shinde, P., & Ingle, Y. (2024). Skin Cancer Detection: A Review Using Machine Learning Techniques. Asian Journal of Research in Computer Science, 17(2), 15–26. doi:10.9734/ajrcos/2024/v17i2416.

Chakkarapani, V., & Poornapushpakala, S. (2024). Chronological Dingo Optimizer-based Deep Maxout Network for skin cancer detection and skin lesion segmentation using Double U-Net. Multimedia Tools and Applications, 83, 71235–71263. doi:10.1007/s11042-024-18229-5.

Likhar, K., & Ridhorkar, S. (2024). Enhancing Skin Cancer Detection: A Comparative Analysis of Models with VGG-16, VGG-19, and Inception V3. International Journal of Intelligent Systems and Applications in Engineering, 12(10s), 502–514.

Moturi, D., Surapaneni, R. K., & Avanigadda, V. S. G. (2024). Developing an efficient method for melanoma detection using CNN techniques. Journal of the Egyptian National Cancer Institute, 36(1). doi:10.1186/s43046-024-00210-w.

Sharma, P., & Suji, J. (2016). A Review on Image Segmentation with its Clustering Techniques. International Journal of Signal Processing, Image Processing and Pattern Recognition, 9(5), 209–218. doi:10.14257/ijsip.2016.9.5.18.

Zhao, S., Gu, C., Yu, J., Akashi, T., & Zhang, C. (2024). TriClick: Interactive Dermoscopic Image Segmentation with Triangle Map. IEEJ Transactions on Electrical and Electronic Engineering, 19(5), 733–744. doi:10.1002/tee.24020.

Abualigah, L., Al-Okbi, N. K., Awwad, E. M., Sharaf, M., & Daoud, M. S. (2024). Correction to: Boosted Aquila Arithmetic Optimization Algorithm for multi-level thresholding image segmentation. Evolving Systems, 15(4), 1399-1427. doi:10.1007/s12530-024-09576-7.

Ram, P., & Padmavathi, S. (2017). Analysis of Harris corner detection for color images. In International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 - Proceedings. doi:10.1109/SCOPES.2016.7955862.

Haggui, O., Tadonki, C., Lacassagne, L., Sayadi, F., & Ouni, B. (2018). Harris corner detection on a NUMA manycore. Future Generation Computer Systems, 88, 442–452. doi:10.1016/j.future.2018.01.048.

Zhou, P., Zhang, H., & Liang, W. (2023). Research on hybrid intrusion detection based on improved Harris Hawk optimization algorithm. Connection Science, 35(1), 2195595. doi:10.1080/09540091.2023.2195595.

Li, Y. B., & Li, J. J. (2011). Harris corner detection algorithm based on improved contourlet transform. Procedia Engineering, 15, 2239–2243. doi:10.1016/j.proeng.2011.08.419.

Han, X., Wang, X., Leng, Y., & Zhou, W. (2021). A plane extraction approach in inverse depth images based on region-growing. Sensors (Switzerland), 21(4), 1–15. doi:10.3390/s21041141.

Adams, R., & Bischof, L. (1994). Seeded Region Growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), 641–647. doi:10.1109/34.295913.

Poux, F., Mattes, C., Selman, Z., & Kobbelt, L. (2022). Automatic region-growing system for the segmentation of large point clouds. Automation in Construction, 138. doi:10.1016/j.autcon.2022.104250.

Arjun, K. P., & Kumar, K. S. (2022). A combined approach of VGG 16 and LSTM transfer learning technique for skin melanoma classification. International Journal of Health Sciences, 13504–13516. doi:10.53730/ijhs.v6ns1.8378.

Jiang, L., Sun, X., Mercaldo, F., & Santone, A. (2020). DECAB-LSTM: Deep Contextualized Attentional Bidirectional LSTM for cancer hallmark classification. Knowledge-Based Systems, 210. doi:10.1016/j.knosys.2020.106486.

Roy, S. S., Awad, A. I., Amare, L. A., Erkihun, M. T., & Anas, M. (2022). Multimodel Phishing URL Detection Using LSTM, Bidirectional LSTM, and GRU Models. Future Internet, 14(11). doi:10.3390/fi14110340.

Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017). LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2222–2232. doi:10.1109/TNNLS.2016.2582924.

Sherstinsky, A. (2020). Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Physica D: Nonlinear Phenomena, 404. doi:10.1016/j.physd.2019.132306.

Wang, Y., & Zhang, W. (2021). A Dense RNN for Sequential Four-Chamber View Left Ventricle Wall Segmentation and Cardiac State Estimation. Frontiers in Bioengineering and Biotechnology, 9(August), 1–8. doi:10.3389/fbioe.2021.696227.

Müller, D., Hartmann, D., Meyer, P., Auer, F., Soto-Rey, I., & Kramer, F. (2022). MISeval: A Metric Library for Medical Image Segmentation Evaluation. In Studies in Health Technology and Informatics, 294, 33–37. doi:10.3233/SHTI220391.

Li, J., Udupa, J. K., Tong, Y., Wang, L., & Torigian, D. A. (2021). Segmentation evaluation with sparse ground truth data: Simulating true segmentations as perfect/imperfect as those generated by humans. Medical Image Analysis, 69. doi:10.1016/j.media.2021.101980.

Behura, A. (2021). Congruence of Deep Learning in Medical Image Processing: Future Prospects and Challenges. Studies in Computational Intelligence, 936, 197–221. doi:10.1007/978-981-33-4698-7_10.

Singh, C., Nischitha, Shetty, S. S., Bekal, A., Bhat, S., & Badiger, M. (2024). Deep Learning Analysis for Skin Cancer Detection. Lecture Notes in Electrical Engineering: Vol. 1062 LNEE, 159–171. doi:10.1007/978-981-99-4444-6_12.

Obaid, A. M., Shawkat, A. S., & Abdulhussein, N. S. (2024). A powerful deep learning method for skin cancer detection. Journal of Autonomous Intelligence, 7(1), 1156. doi:10.32629/jai.v7i1.1156.

Lu, Y., Cheung, Y. M., & Tang, Y. Y. (2020). Bayes Imbalance Impact Index: A Measure of Class Imbalanced Data Set for Classification Problem. IEEE Transactions on Neural Networks and Learning Systems, 31(9), 3525–3539. doi:10.1109/TNNLS.2019.2944962.

Zhang, X., Li, R., Zhang, B., Yang, Y., Guo, J., & Ji, X. (2019). An instance-based learning recommendation algorithm of imbalance handling methods. Applied Mathematics and Computation, 351, 204–218. doi:10.1016/j.amc.2018.12.020.

Shilpa Kamdi, R. (2012). Image Segmentation and Region Growing Algorithm. International Journal of Computer Technology and Electronics Engineering, 2(1), 103–107.

Ali, E., Ullah Khan, E., Zarrar Mahmudi, E., & Ullah, R. (2016). A Comparison of FAST, SURF, Eigen, Harris, and MSER Features. International Journal of Computer Engineering and Information Technology, 8(6), 100–105.

Gulzar, Y., & Khan, S. A. (2022). Skin Lesion Segmentation Based on Vision Transformers and Convolutional Neural Networks—A Comparative Study. Applied Sciences (Switzerland), 12(12), 5590. doi:10.3390/app12125990.

Vishnu Priya, A., Singh, H. K., Siva Chaitanya Prasad, M., & Jai Siva Sai, G. (2023). RNN-LSTM Based Deep Learning Model for Tor Traffic Classification. Cyber-Physical Systems, 9(1), 25–42. doi:10.1080/23335777.2021.1924284.

Hayati, M., Muchtar, K., Roslidar, Maulina, N., Syamsuddin, I., Elwirehardja, G. N., & Pardamean, B. (2022). Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning. Procedia Computer Science, 216, 57–66. doi:10.1016/j.procs.2022.12.111.

Imtiaz, I., Ahmed, I., Ahmad, M., Ullah, K., Adnan, A., & Ahmad, M. (2019). Segmentation of Skin Lesion Using Harris Corner Detection and Region Growing. 2019 IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019, 0614–0619. doi:10.1109/UEMCON47517.2019.8993034.

Gowthami, V., & Sneha, G. (2021). Melanoma Detection Using Recurrent Neural Network. Lecture Notes in Electrical Engineering, 700, 1563–1573. doi:10.1007/978-981-15-8221-9_146.

Asyhar, A. H., Foeady, A. Z., Thohir, M., Arifin, A. Z., Haq, D. Z., & Novitasari, D. C. R. (2020). Implementation LSTM Algorithm for Cervical Cancer using Colposcopy Data. 2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, 485–489. doi:10.1109/ICAIIC48513.2020.9065068.

Jain, A., Mittal, N., & Nain, S. (2023). CNN-based Recognition of Skin Cancer Using Contrast Limited Adaptive Histogram Equalization. Lecture Notes in Networks and Systems: Vol. 617 LNNS, 667–678. doi:10.1007/978-981-19-9512-5_61.

Ray, P. J., Priya, S., & Kumar, T. A. (2015). Nuclear segmentation for skin cancer diagnosis from histopathological images. Global Conference on Communication Technologies, GCCT 2015, 397–401. doi:10.1109/GCCT.2015.7342692.

Patra, A., Behera, S. K., & Barpanda, N. K. (2022). Hybrid deep CNN-LSTM network for breast histopathological image classification. Onkologia i Radioterapia, 16(9), 12–15.

Limanto, S., Buliali, J. L., & Saikhu, A. (2024). GLoW SMOTE-D: Oversampling Technique to Improve Prediction Model Performance of Students Failure in Courses. IEEE Access, 12, 8889–8901. doi:10.1109/ACCESS.2024.3351569.


Full Text: PDF

DOI: 10.28991/HIJ-2024-05-03-07

Refbacks



Copyright (c) 2024 Suhendro Yusuf Irianto