2,395 research outputs found
Analyzing Digital Image by Deep Learning for Melanoma Diagnosis
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 AndalucĂa Tech
Machine learning methods for histopathological image analysis
Abundant accumulation of digital histopathological images has led to the
increased demand for their analysis, such as computer-aided diagnosis using
machine learning techniques. However, digital pathological images and related
tasks have some issues to be considered. In this mini-review, we introduce the
application of digital pathological image analysis using machine learning
algorithms, address some problems specific to such analysis, and propose
possible solutions.Comment: 23 pages, 4 figure
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melNET: A Deep Learning Based Model For Melanoma Detection
Melanoma is identified as the deadliest in the skin cancer category. However, early-stage detection may enhance the treatment result. In this research, a deep learning-based model, named “melNET”, has been developed to detect melanoma in both dermoscopic and digital images. melNET uses the Inception-v3 architecture to handle the deep learning part. To ensure quality optimization, the architectural aspects of Inception-v3 were designed using the Hebbian principle as well as taking the intuition of multi-scale processing. This architecture takes advantage of parallel computing across multiple GPUs to employ RMSprop as the optimizer. While going through the training phase, melNET uses the back-propagation method to retrain this Inception-v3 network by feeding the errors from each iteration, resulting in the fine-tuning of network weights. After the completion of the training step, melNET can be used to predict the diagnosis of a mole by taking the lesion image as an input to the system. With a dermoscopic dataset of 200 images, provided by PH2, melNET outperforms the work with YOLO-v2 network by improving the sensitivity value from 86.35% to 97.50%. Also, the specificity and accuracy values are found to be improved from 85.90% to 87.50%, and, from 86.00% to 89.50% respectively. melNET has also been evaluated on a digital dataset of 170 images, provided by UMCG, showing an accuracy of 84.71%, which outperforms the 81.00% accuracy of the MED-NODE model. In both cases, melNET got treated as a binary classifier and a five-fold cross validation method was applied for the evaluation. In addition, melNET has been found to perform the detections in real-time by leveraging the end-to-end Inception-v3 architecture
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