5,647 research outputs found

    The Melanoma Skin Cancer Detection Using Convolutional Neural Network

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    The most common cause of death for persons in the modern world is melanoma skin cancer. Melanoma is an aggressive kind of skin cancer that typically develops on parts of the body exposed to sunlight, UV radiation, dust, pollution, and microbes. A study found that 79% of humans who do have melanoma skin cancer in its early stages are unaware of having it. When it is finally identified, it may have progressed further into the skin and may have impacted other parts of the body, making treatment extremely difficult and it also makes the survival rate for humans very low. As a result, melanoma skin cancer kills the majority of its victims. If melanoma is identified or recognized in the early stages, then it could have been cured easily and it also has a bigger survival rate for humans. There is an automated system that has been designed with the compilation of data sets with a variety of diagnoses. This automated system will help to detect in early stages of melanoma. Convolutional neural network technology was initially employed in our automated system to classify data. But as a result, our accuracy was poor. Then, we used support vector machines (SVMs) to analyze the entire dataset after categorizing and segmenting the data into zones, using Tensor Flow libraries to implement the entire model. After using the model, we achieve a 96% accuracy rate

    Cancer diagnosis using deep learning: A bibliographic review

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    In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

    Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection

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    While skin cancer is the most diagnosed form of cancer in men and women, with more cases diagnosed each year than all other cancers combined, sufficiently early diagnosis results in very good prognosis and as such makes early detection crucial. While radiomics have shown considerable promise as a powerful diagnostic tool for significantly improving oncological diagnostic accuracy and efficiency, current radiomics-driven methods have largely rely on pre-defined, hand-crafted quantitative features, which can greatly limit the ability to fully characterize unique cancer phenotype that distinguish it from healthy tissue. Recently, the notion of discovery radiomics was introduced, where a large amount of custom, quantitative radiomic features are directly discovered from the wealth of readily available medical imaging data. In this study, we present a novel discovery radiomics framework for skin cancer detection, where we leverage novel deep multi-column radiomic sequencers for high-throughput discovery and extraction of a large amount of custom radiomic features tailored for characterizing unique skin cancer tissue phenotype. The discovered radiomic sequencer was tested against 9,152 biopsy-proven clinical images comprising of different skin cancers such as melanoma and basal cell carcinoma, and demonstrated sensitivity and specificity of 91% and 75%, respectively, thus achieving dermatologist-level performance and \break hence can be a powerful tool for assisting general practitioners and dermatologists alike in improving the efficiency, consistency, and accuracy of skin cancer diagnosis

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

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    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

    Analyzing Digital Image by Deep Learning for Melanoma Diagnosis

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    Image classi cation is an important task in many medical applications, in order to achieve an adequate diagnostic of di erent le- sions. Melanoma is a frequent kind of skin cancer, which most of them can be detected by visual exploration. Heterogeneity and database size are the most important di culties to overcome in order to obtain a good classi cation performance. In this work, a deep learning based method for accurate classi cation of wound regions is proposed. Raw images are fed into a Convolutional Neural Network (CNN) producing a probability of being a melanoma or a non-melanoma. Alexnet and GoogLeNet were used due to their well-known e ectiveness. Moreover, data augmentation was used to increase the number of input images. Experiments show that the compared models can achieve high performance in terms of mean ac- curacy with very few data and without any preprocessing.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
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