Melanoma Diagnosis Using Deep Learning and Fuzzy Logic

Melanoma or malignant melanoma is a type of skin cancer that develops when melanocyte cells, damaged by excessive exposure to harmful UV radiations, start to grow out of control. Though less common than some other kinds of skin cancers, it is more dangerous because it rapidly metastasizes if not diagnosed and treated at an early stage. The distinction between benign and melanocytic lesions could at times be perplexing, but the manifestations of the disease could fairly be distinguished by a skilled study of its histopathological and clinical features. In recent years, deep convolutional neural networks (DCNNs) have succeeded in achieving more encouraging results yet faster and computationally effective systems for detection of the fatal disease are the need of the hour. This paper presents a deep learning-based ‘You Only Look Once (YOLO)’ algorithm, which is based on the application of DCNNs to detect melanoma from dermoscopic and digital images and offer faster and more precise output as compared to conventional CNNs. In terms with the location of the identified object in the cell, this network predicts the bounding box of the detected object and the class confidence score. The highlight of the paper, however, lies in its infusion of certain resourceful concepts like two phase segmentation done by a combination of the graph theory using minimal spanning tree concept and L-type fuzzy number based approximations and mathematical extraction of the actual affected area of the lesion region during feature extraction process. Experimented on a total of 20250 images from three publicly accessible datasets—PH2, International Symposium on Biomedical Imaging (ISBI) 2017 and The International Skin Imaging Collaboration (ISIC) 2019, encouraging results have been obtained. It achieved a Jac score of 79.84% on ISIC 2019 dataset and 86.99% and 88.64% on ISBI 2017 and PH2 datasets, respectively. Upon comparison of the pre-defined parameters with recent works in this area yielded comparatively superior output in most cases.

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Diagnosis of Melanoma Lesion Using Neutrosophic and Deep Learning

Traitement du signal ◽

10.18280/ts.380507 ◽

2021 ◽

Vol 38 (5) ◽

pp. 1327-1338

Author(s):

Shubhendu Banerjee ◽

Sumit Kumar Singh ◽

Avishek Chakraborty ◽

Sharmistha Basu ◽

Atanu Das ◽

...

Keyword(s):

Deep Learning ◽

Accurate Result ◽

Recent Past ◽

Computationally Efficient ◽

Deep Convolutional Neural Networks ◽

Skin Imaging ◽

Melanocytic Lesions ◽

Skin Cancers ◽

Straight Line ◽

Efficient Mechanisms

Melanoma is a kind of skin cancer which occurs due to too much exposure of melanocyte cells to the dangerous UV radiations, that gets damaged and multiplies uncontrollably. This is popularly known as malignant melanoma and is comparatively less heard of than certain other types of skin cancers; however it can be more detrimental as it swiftly spreads if not detected and attended at a primary stage. The differentiation between benign and melanocytic lesions sometimes may be confusing, but the symptoms of the disease can reasonably be discriminated by a profound investigation of its histopathological and clinical characteristics. In the recent past, Deep Convolutional Neural Networks (DCNNs) have advanced in accomplishing far better results. The necessity of the present day is to have faster and computationally efficient mechanisms for diagnosis of the deadly disease. This paper makes an effort to showcase a deep learning-based ‘Keras’ algorithm, which is established on the implementation of DCNNs to investigate melanoma from dermoscopic and digital pictures and provide swifter and more accurate result as contrasted to standard CNNs. The main highlight of this paper, basically stands in its incorporation of certain ambitious notions like the segmentation performed by a culmination of a moving straight line with a sequence of points and the application of the concept of triangular neutrosophic number based on uncertain parameters. The experiment was done on a total of 40,676 images obtained from four commonly available datasets— International Symposium on Biomedical Imaging (ISBI) 2017, International Skin Imaging Collaboration (ISIC) 2018, ISIC 2019 and ISIC 2020 and the end result received was indeed motivating. It attained a Jac score of 86.81% on ISIC 2020 dataset and 95.98%, 95.66% and 94.42% on ISBI 2017, ISIC 2018 and ISIC 2019 datasets, respectively. The present research yielded phenomenal output in most instances in comparison to the pre-defined parameters with the similar types of works in this field.

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Thermal-based early breast cancer detection using inception V3, inception V4 and modified inception MV4

Neural Computing and Applications ◽

10.1007/s00521-021-06372-1 ◽

2021 ◽

Author(s):

Mohammed Abdulla Salim Al Husaini ◽

Mohamed Hadi Habaebi ◽

Teddy Surya Gunawan ◽

Md Rafiqul Islam ◽

Elfatih A. A. Elsheikh ◽

...

Keyword(s):

Breast Cancer ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Early Stage ◽

Computational Cost ◽

Optimization Methods ◽

Optimization Method ◽

Deep Convolutional Neural Networks ◽

Breast Thermography

AbstractBreast cancer is one of the most significant causes of death for women around the world. Breast thermography supported by deep convolutional neural networks is expected to contribute significantly to early detection and facilitate treatment at an early stage. The goal of this study is to investigate the behavior of different recent deep learning methods for identifying breast disorders. To evaluate our proposal, we built classifiers based on deep convolutional neural networks modelling inception V3, inception V4, and a modified version of the latter called inception MV4. MV4 was introduced to maintain the computational cost across all layers by making the resultant number of features and the number of pixel positions equal. DMR database was used for these deep learning models in classifying thermal images of healthy and sick patients. A set of epochs 3–30 were used in conjunction with learning rates 1 × 10–3, 1 × 10–4 and 1 × 10–5, Minibatch 10 and different optimization methods. The training results showed that inception V4 and MV4 with color images, a learning rate of 1 × 10–4, and SGDM optimization method, reached very high accuracy, verified through several experimental repetitions. With grayscale images, inception V3 outperforms V4 and MV4 by a considerable accuracy margin, for any optimization methods. In fact, the inception V3 (grayscale) performance is almost comparable to inception V4 and MV4 (color) performance but only after 20–30 epochs. inception MV4 achieved 7% faster classification response time compared to V4. The use of MV4 model is found to contribute to saving energy consumed and fluidity in arithmetic operations for the graphic processor. The results also indicate that increasing the number of layers may not necessarily be useful in improving the performance.

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OPTIMISING CLASSIFICATION TECHNIQUES FOR LUNG CANCER DETECTION ON CT IMAGES

EPRA International Journal of Multidisciplinary Research (IJMR) ◽

10.36713/epra4214 ◽

2020 ◽

pp. 206-209

Author(s):

T. Maria Patricia Peeris ◽

Prof. P. Brundha

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Early Diagnosis ◽

Cancer Detection ◽

Early Stage ◽

Extraction Process ◽

Ct Images ◽

Vital Role ◽

Classification Techniques ◽

Classification Prediction

Lungs are the most crucial organs in a human body. Since the cancer detection began, lung cancer has been the most common terminal disease amongst all type of cancers. The contribution of deep learning, especially the convolution neural networks has widely reduced the mortality rates resulting from lung cancer. The classification of Computed Tomography (CT) images has enhanced the early diagnosis of lung cancer that has enabled victims to undergo treatment at an early stage. The resolution of the CT images have been variedly used for the accuracy of the model. Besides, the detection of lumps or anomalies in the images has greatly supported early diagnosis. Classification plays a vital role in the deep learning models to sort out the input images as positive and negative based on the attribute of the model built. However, the generalisation of classifiers has reduced the accuracy of the corresponding models built. To increase the accuracy and efficiency of the deep learning model, an optimised classification technique is used to predict lung cancer from the CT images. The purpose of optimisation here will enable the model to adapt stipulated feature extraction process according to the input images fed into the network. The model will be trained for predicting purpose given any resolution of the images. KEYWORDS: Lung cancer, CT images, Classification techniques, Optimised Classification, Prediction

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Deep learning in Dermatology for skin Diseases Detection.

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f8498.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 3929-3933

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Early Detection ◽

Cell Carcinoma ◽

Skin Diseases ◽

Early Stage ◽

Medical Field ◽

Home Remedy ◽

Disease Symptoms ◽

Skin Cancers

Dermatology is a medical field that treats skin health and diseases. People feeling disease symptoms of an affecting the skin must consult a dermatologist if this stipulation does not respond to home remedy. Early detection and treatment can correct most skin disorders. Basal Cell Carcinoma (BCC), Melanoma and Squamous Cell Carcinoma (SCC) are typically appearing type of skin cancers. The purpose of this effort is to provide a system that can be deployed to classify dermatoscopic images to predict skin diseases with early detection and higher accuracy . This work is a concrete effort to accomplish higher degree of accuracy for clinical usage by implementing advances in soft computing and image processing like deep learning and in-depth neural networks in an early stage for 7 class classification for HAM10000 dataset.

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Grape Bunch Detection at Different Growth Stages Using Deep Learning Quantized Models

Agronomy ◽

10.3390/agronomy11091890 ◽

2021 ◽

Vol 11 (9) ◽

pp. 1890

Author(s):

André Silva Aguiar ◽

Sandro Augusto Magalhães ◽

Filipe Neves dos Santos ◽

Luis Castro ◽

Tatiana Pinho ◽

...

Keyword(s):

Deep Learning ◽

Low Power ◽

Agricultural Sector ◽

Early Stage ◽

Low Cost ◽

Confidence Score ◽

Growth Stages ◽

Processing Unit ◽

Single Shot ◽

Different Growth Stages

The agricultural sector plays a fundamental role in our society, where it is increasingly important to automate processes, which can generate beneficial impacts in the productivity and quality of products. Perception and computer vision approaches can be fundamental in the implementation of robotics in agriculture. In particular, deep learning can be used for image classification or object detection, endowing machines with the capability to perform operations in the agriculture context. In this work, deep learning was used for the detection of grape bunches in vineyards considering different growth stages: the early stage just after the bloom and the medium stage where the grape bunches present an intermediate development. Two state-of-the-art single-shot multibox models were trained, quantized, and deployed in a low-cost and low-power hardware device, a Tensor Processing Unit. The training input was a novel and publicly available dataset proposed in this work. This dataset contains 1929 images and respective annotations of grape bunches at two different growth stages, captured by different cameras in several illumination conditions. The models were benchmarked and characterized considering the variation of two different parameters: the confidence score and the intersection over union threshold. The results showed that the deployed models could detect grape bunches in images with a medium average precision up to 66.96%. Since this approach uses low resources, a low-cost and low-power hardware device that requires simplified models with 8 bit quantization, the obtained performance was satisfactory. Experiments also demonstrated that the models performed better in identifying grape bunches at the medium growth stage, in comparison with grape bunches present in the vineyard after the bloom, since the second class represents smaller grape bunches, with a color and texture more similar to the surrounding foliage, which complicates their detection.

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Design and Implement of Deep Learning Model to Detect the Melanoma

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e6611.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 3497-3504

Keyword(s):

Deep Learning ◽

Skin Cancer ◽

Clustering Algorithm ◽

Early Stage ◽

Skin Lesions ◽

The Body ◽

Low Contrast ◽

Skin Imaging ◽

Non Melanoma Skin Cancer ◽

Initial Stage

Detecting Skin lesions on the human body is a big task to the doctors in the initial stage because of the low contrast on the body. This skin cancer can be occur due to sun rays. If the disease cannot detect in early stage, there it may cause death to human lives. Here there are some algorithms to predict the melanoma using deep learning techniques. ISIC International Skin Imaging Collaboration Archive set where it provides various images of melanoma and non-melanoma. There are so many challenges to identify the image with melanoma and non-melanoma types of skin cancer. In this paper we applied hair removal algorithm and k-means clustering algorithm where to remove unwanted substances from the original images. To classify the melanoma and non-melanoma skin cancer, this paper proposed prediction process and sequential CNN architecture.

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Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

Author(s):

Avinash Chandra Pandey ◽

Dharmveer Singh Rajpoot

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Short Term Memory ◽

Computational Cost ◽

Extraction Process ◽

Learning Model ◽

Sentiment Classification ◽

Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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Deep convolutional neural networks for cardiovascular vulnerable plaque detection

MATEC Web of Conferences ◽

10.1051/matecconf/201927702024 ◽

2019 ◽

Vol 277 ◽

pp. 02024 ◽

Cited By ~ 1

Author(s):

Lincan Li ◽

Tong Jia ◽

Tianqi Meng ◽

Yizhe Liu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Vulnerable Plaque ◽

Recall Rate ◽

Superior Performance ◽

Learning Approaches ◽

Deep Convolutional Neural Networks ◽

Vulnerable Plaques ◽

Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Sensors ◽

10.3390/s21030863 ◽

2021 ◽

Vol 21 (3) ◽

pp. 863

Author(s):

Vidas Raudonis ◽

Agne Paulauskaite-Taraseviciene ◽

Kristina Sutiene

Keyword(s):

Deep Learning ◽

Image Fusion ◽

Early Stage ◽

Image Data ◽

Cell Detection ◽

Processing Times ◽

Fused Image ◽

Stage Embryo ◽

Early Stage Embryo

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.

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