168 129
Full Length Article
Fusion: Practice and Applications
Volume 8 , Issue 2, PP: 08-15 , 2022 | Cite this article as | XML | Html |PDF


Skin Cancer Detection Using Deep Learning and Artificial Intelligence: Incorporated model of deep features fusion

Authors Names :   Ahmed Abdelaziz   1 *     Alia N. Mahmoud   2  

1  Affiliation :  Nova Information Management School, Universidade Nova de Lisboa, 1070-312, Lisboa, Portugal

    Email :  D20190535@novaims.unl.pt

2  Affiliation :  Nova Information Management School, Universidade Nova de Lisboa, 1070-312, Lisboa, Portugal

    Email :  M20190508@novaims.unl.pt

Doi   :   https://doi.org/10.54216/FPA.080201

Received: March 26, 2022 Accepted: August 23, 2022

Abstract :

Among the most frequent forms of cancer, skin cancer accounts for hundreds of thousands of fatalities annually throughout the globe. It shows up as excessive cell proliferation on the skin. The likelihood of a successful recovery is greatly enhanced by an early diagnosis. More than that, it might reduce the need for or the frequency of chemical, radiological, or surgical treatments. As a result, savings on healthcare expenses will be possible. Dermoscopy, which examines the size, form, and color features of skin lesions, is the first step in the process of detecting skin cancer and is followed by sample and lab testing to confirm any suspicious lesions. Deep learning AI has allowed for significant progress in image-based diagnostics in recent years. Deep neural networks known as convolutional neural networks (CNNs or ConvNets) are essentially an extended form of multi-layer perceptrons. In visual imaging challenges, CNNs have shown the best accuracy. The purpose of this research is to create a CNN model for the early identification of skin cancer. The backend of the CNN classification model will be built using Keras and Tensorflow in Python. Different network topologies, such as Convolutional layers, Dropout layers, Pooling layers, and Dense layers, are explored and tried out throughout the model's development and validation phases. Transfer Learning methods will also be included in the model to facilitate early convergence. The dataset gathered from the ISIC challenge archives will be used to both tests and train the model.

Keywords :

Skin Cancer; Deep Learning; Image Classification; Neural Network 

References :

[1] da Silva, G. L. F., da Silva Neto, O. P., Silva, A. C., de Paiva, A. C. & Gattass, M. Lung nodules

diagnosis based on evolutionary convolutional neural network. Multimedia Tools and Applications 76,

19039–19055 (2017).

[2] Garbe, C. et al. Epidemiology of cutaneous melanoma and keratinocyte cancer in white populations

1943–2036. European Journal of Cancer 152, 18–25 (2021).

[3] Karimkhani, C., Boyers, L. N., Dellavalle, R. P. & Weinstock, M. A. It’s time for “keratinocyte

carcinoma” to replace the term “nonmelanoma skin cancer”. Journal of the American Academy of

Dermatology 72, 186–187 (2015).

[4] Griffin, L. L., Ali, F. R. & Lear, J. T. Non-melanoma skin cancer. Clinical medicine 16, 62 (2016).

[5] Karia, P. S. Epidemiology and outcomes of cutaneous squamous cell carcinoma. in High-risk cutaneous

squamous cell carcinoma 3–28 (Springer, 2016).

[6] Phillips, M. et al. Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in

images of skin lesions. JAMA network open 2, e1913436–e1913436 (2019).

[7] Asada, K. et al. Uncovering prognosis-related genes and pathways by multi-omics analysis in lung

cancer. Biomolecules 10, 524 (2020).

[8] Hamamoto, R., Komatsu, M., Takasawa, K., Asada, K. & Kaneko, S. Epigenetics analysis and

integrated analysis of multiomics data, including epigenetic data, using artificial intelligence in the era

of precision medicine. Biomolecules 10, 62 (2019).

[9] Yamada, M. et al. Development of a real-time endoscopic image diagnosis support system using deep

learning technology in colonoscopy. Scientific reports 9, 1–9 (2019).

[10] Yasutomi, S., Arakaki, T. & Hamamoto, R. Shadow detection for ultrasound images using unlabeled

data and synthetic shadows. arXiv preprint arXiv:1908.01439 (2019).

[11] Ren, S., He, K., Girshick, R. & Sun, J. Faster r-cnn: Towards real-time object detection with region

proposal networks. Advances in neural information processing systems 28, (2015).

[12] Liu, T. & Stathaki, T. Faster R-CNN for robust pedestrian detection using semantic segmentation

network. Frontiers in neurorobotics 12, 64 (2018).

[13] Girshick, R., Donahue, J., Darrell, T. & Malik, J. Rich feature hierarchies for accurate object detection

and semantic segmentation. in Proceedings of the IEEE conference on computer vision and pattern

recognition 580–587 (2014).

[14] Shao, F. et al. Improved faster R-CNN traffic sign detection based on a second region of interest and

highly possible regions proposal network. Sensors 19, 2288 (2019).

[15] Huang, Y.-Q., Zheng, J.-C., Sun, S.-D., Yang, C.-F. & Liu, J. Optimized YOLOv3 algorithm and its

application in traffic flow detections. Applied Sciences 10, 3079 (2020).

[16] Mittal, S. A Survey on optimized implementation of deep learning models on the NVIDIA Jetson

platform. Journal of Systems Architecture 97, 428–442 (2019).

[17] Kuok, C., Horng, M., Liao, Y., Chow, N. & Sun, Y. An effective and accurate identification system of

Mycobacterium tuberculosis using convolution neural networks. Microscopy research and technique

82, 709–719 (2019).

[18] Rosati, R. et al. Faster R-CNN approach for detection and quantification of DNA damage in comet

assay images. Computers in Biology and Medicine 123, 103912 (2020).

[19] Goyal, M., Reeves, N. D., Rajbhandari, S. & Yap, M. H. Robust methods for real-time diabetic foot

ulcer detection and localization on mobile devices. IEEE journal of biomedical and health informatics

23, 1730–1741 (2018).

[20] Lee, N., Caban, J., Ebadollahi, S. & Laine, A. Interactive segmentation in multi-modal brain imagery

using a bayesian transductive learning approach. in Medical Imaging 2009: Computer-Aided Diagnosis

vol. 7260 546–555 (SPIE, 2009).

[21] Wan, S., Mak, M.-W. & Kung, S.-Y. Transductive learning for multi-label protein subchloroplast

localization prediction. IEEE/ACM transactions on computational biology and bioinformatics 14, 212–

224 (2016).

[22] Buechi, R. et al. Evidence assessing the diagnostic performance of medical smartphone apps: a

systematic review and exploratory meta-analysis. BMJ open 7, e018280 (2017).

Cite this Article as :
Ahmed Abdelaziz , Alia N. Mahmoud, Skin Cancer Detection Using Deep Learning and Artificial Intelligence: Incorporated model of deep features fusion, Fusion: Practice and Applications, Vol. 8 , No. 2 , (2022) : 08-15 (Doi   :  https://doi.org/10.54216/FPA.080201)