Classification of skin cancer images using TensorFlow  and inception v3

The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.

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“…Transfer learning concept in Inception-V3 extracts bottleneck 2048η dimensional vector features from each frame. [86][87][88][89]…”

Section: Inception-v3 Convolutional Architecturementioning

confidence: 99%

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

et al. 2020

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“…(ii) Fine tuning which is trying to unfreeze a few layers from the pre-trained model and training them together with the new classification layer [16][17] [18]. Fig.…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

“…Some of the areas where CNNs have been used in agriculture include crop type classification, plant disease detection and recognition and so on [11]. In [18], the authors developed a model for predicting skin cancer cells by image classification using CNN and obtained more than 85% prediction accuracy and [23] tried to automate the classification of ovarian cancer using CNN to aid pathologists in cancer diagnosis. They used CNN based on AlexNet to predict ovarian cancer from cytological images.…”

Section: B Cnn Application In Classification Problemsmentioning

confidence: 99%

A Convolutional Neural Network for Automatic Identification and Classification of Fall Army Worm Moth

Chulu¹,

Phiri²,

Nkunika³

et al. 2019

IJACSA

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To combat the problem caused by the Fall Army Worm in the country there is a need to come up with robust early warning and monitoring systems as the current manual system is labor intensive and time consuming. The automation of the identification and classification of the insect is one of the novel methods that can be undertaken. Therefore this paper presents the results of training a Convolutional Neural Network model using Google's Tensorflow Deep Learning Framework for the identification and classification of the Fall Army worm moth. Due to lack of enough training dataset and good computing power, we used transfer learning, which is the process of reusing a model trained on one task as a starting point for a model on a second task. Googles pre-trained InceptionV3 model was used as the underlying model. Data was collected from four sources namely the field, Lab setup, by crawling the internet and using Data Augmentation. We Present results of the best three trials in terms of training accuracy after several attempts to get the best metrics in terms of learning rate and training steps. The best model gave a prediction average accuracy of 82% and a 32% average prediction accuracy on false positives. The results shows that it is possible to automate the identification and classification of the Fall Army worm Moth using Convolutional Neural Networks.

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“…There are different convolution nets available like VGG 16, VGG19, inception V3, Alexnet [16], [17]. For some of the nets code is already written in keras and for some, code need to be written in python.…”

Section: B Convolution Neural Networkmentioning

confidence: 99%

Prediction of Caption and Emoji of an Image using Deep Learning

2019

IJRTE

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With the invention of deep learning, there is a good progress in image classification. But automatic generation of captions for images is still a challenging problem and is in the initial stages of artificial intelligence research. Automatic description of images has applications in social networking and will be useful to visually impaired persons. This paper concentrates on designing a user-friendly web application framework which can predict the caption of an image using deep learning techniques. The verbs and objects present in the caption are used for forming the emoji and for predicting the major color of the image

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Classification of skin cancer images using TensorFlow and inception v3

Cited by 16 publications

References 8 publications

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

A Convolutional Neural Network for Automatic Identification and Classification of Fall Army Worm Moth

Prediction of Caption and Emoji of an Image using Deep Learning

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