Computerized diagnosis of skin cancer using neural networks

The objective of this research is provide to the specialists in skin cancer, a premature, rapid and non-invasive diagnosis of melanoma identification, using an image of the lesion, to apply to the treatment of a patient, the method used is the architecture contrast of Convolutional neural networks proposed by Laura Kocobinski of the University of Boston, against our architecture, which reduce the depth of the convolution filter of the last two convolutional layers to obtain maps of more significant characteristics. The performance of the model was reflected in the accuracy during the validation, considering the best result obtained, which is confirmed with the additional data set. The findings found with the application of this base architecture were improved accuracy from 0.79 to 0.83, with 30 epochs, compared to Kocobinski's AlexNet architecture, it was not possible to improve the accuracy of 0.90, however, the complexity of the network played an important role in the results we obtained, which was able to balance and obtain better results without increasing the epochs, the application of our research is very helpful for doctors, since it will allow them to quickly identify if an injury is melanoma or not and consequently treat it efficiently.

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Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

Journal of Applied Science and Technology Trends ◽

10.38094/jastt20189 ◽

2021 ◽

Vol 2 (01) ◽

pp. 41-51

Author(s):

Jwan Saeed ◽

Subhi Zeebaree

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Skin Cancer ◽

Cancer Detection ◽

Deep Convolutional Neural Networks ◽

Color Structure ◽

Malignant Skin ◽

Dermatological Disorders ◽

Detection And Diagnosis ◽

Skin Cancer Detection

Skin cancer is among the primary cancer types that manifest due to various dermatological disorders, which may be further classified into several types based on morphological features, color, structure, and texture. The mortality rate of patients who have skin cancer is contingent on preliminary and rapid detection and diagnosis of malignant skin cancer cells. Limitations in current dermoscopic images, including shadow, artifact, and noise, affect image quality, which may hamper detection effort. Attempts to overcome these challenges have been made by analyzing the images using deep learning neural networks to perform skin cancer detection. In this paper, the authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification.

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Automatic Diagnosis of Skin Cancer Using Neural Networks

2019 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE) ◽

10.1109/atee.2019.8724938 ◽

2019 ◽

Cited By ~ 6

Author(s):

Serban Radu Stefan Jianu ◽

Loretta Ichim ◽

Dan Popescu

Keyword(s):

Neural Networks ◽

Skin Cancer ◽

Automatic Diagnosis

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Erratum: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks

Nature ◽

10.1038/nature22985 ◽

2017 ◽

Vol 546 (7660) ◽

pp. 686-686 ◽

Cited By ~ 49

Author(s):

Andre Esteva ◽

Brett Kuprel ◽

Roberto A. Novoa ◽

Justin Ko ◽

Susan M. Swetter ◽

...

Keyword(s):

Neural Networks ◽

Skin Cancer ◽

Deep Neural Networks

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Skin Cancer Classification Using Convolutional Neural Networks

10.47611/harp.95 ◽

2021 ◽

Author(s):

Trisha Raj

Keyword(s):

Neural Networks ◽

Skin Cancer ◽

Convolutional Neural Networks ◽

Cancer Classification

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Convolutional Neural Networks Model in Premature Detection of Melanoma

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.l1002.10812s19 ◽

2019 ◽

Vol 8 (12S2) ◽

pp. 6-11

Keyword(s):

Neural Networks ◽

Skin Cancer ◽

Convolutional Neural Networks ◽

Additional Data ◽

Data Set ◽

Non Invasive ◽

Convolution Filter ◽

The University ◽

Improved Accuracy

The objective of this research is provide to the specialists in skin cancer, a premature, rapid and non-invasive diagnosis of melanoma identification, using an image of the lesion, to apply to the treatment of a patient, the method used is the architecture contrast of Convolutional neural networks proposed by Laura Kocobinski of the University of Boston, against our architecture, which reduce the depth of the convolution filter of the last two convolutional layers to obtain maps of more significant characteristics. The performance of the model was reflected in the accuracy during the validation, considering the best result obtained, which is confirmed with the additional data set. The findings found with the application of this base architecture were improved accuracy from 0.79 to 0.83, with 30 epochs, compared to Kocobinski's AlexNet architecture, it was not possible to improve the accuracy of 0.90, however, the complexity of the network played an important role in the results we obtained, which was able to balance and obtain better results without increasing the epochs, the application of our research is very helpful for doctors, since it will allow them to quickly identify if an injury is melanoma or not and consequently treat it efficiently.

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Deteksi Dini Kanker Kulit menggunakan K-NN dan Convolutional Neural Network

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020702602 ◽

2020 ◽

Vol 7 (2) ◽

pp. 373

Author(s):

Teresia R. Savera ◽

Winsya H. Suryawan ◽

Agung Wahyu Setiawan

Keyword(s):

Neural Network ◽

Neural Networks ◽

Skin Cancer ◽

Convolutional Neural Network ◽

Learning Algorithm ◽

Detection Systems ◽

Deep Learning Algorithm ◽

Artificial Neural ◽

Processing Techniques ◽

Further Development

Kanker kulit adalah salah satu jenis kanker yang dapat menyebabkan kematian sehingga diperlukan sebuah aplikasi perangkat lunak yang dapat digunakan untuk membantu melakukan deteksi dini kanker kulit dengan mudah. Sehingga diharapkan deteksi dini kanker kulit dapat terdeteksi lebih cepat. Pada penelitian ini terdapat dua metode yang digunakan untuk melakukan deteksi dini kanker kulit yaitu deteksi dengan klasifikasi secara regresi dan artificial neural network dengan arsitektur convolutional neural network. Akurasi yang diperoleh dengan menggunakan klasifikasi secara regresi adalah sebesar 75%. Sementara, akurasi deteksi yang didapatkan dengan menggunakan convolutional neural network adalah sebesar 76%. Hasil yang diperoleh dari kedua metoda ini masih dapat ditingkatkan pada penelitian lanjutan, yaitu dengan cara melakukan prapengolahan pada set data citra yang digunakan. Sehingga set data yang digunakan memiliki tingkat pencahayaan, sudut (pengambilan), serta ukuran citra yang sama. Apabila tersedia sumber daya komputasi yang besar, akan dilakukan penambahan jumlah citra yang digunakan, baik itu sebagai set data latih maupun uji. AbstractSkin cancer is one type of cancer that can cause death for many people. Because of this, an application is needed to easily detect skin cancer early that the cancer can be handled with more quickly. In this study there were two methods used to detect skin cancer, namely detection by regression classification and detection by classifying using artificial neural networks with network convolutional architecture. Detection with regression classification gives an accuracy of 75%. While detection using convolutional neural networks gives an accuracy of 76%. These proposed early detection systems can be improved to increase the accuracy. For further development, several image processing techniques will be applied, such as contrast enhancement and color equalization. For future works, if there is more computational resource, more images can be used as dataset and implement the deep learning algorithm to improve the accuracy.

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