scholarly journals Hyperparameter Optimization of Deep Learning Networks for Classification of Breast Histopathology Images

2021 ◽  
Vol 33 (1) ◽  
pp. 315
Author(s):  
Cheng-Jian Lin ◽  
Shiou-Yun Jeng ◽  
Chin-Ling Lee
Keyword(s):  
Deep Learning ◽  
Learning Networks ◽  
Hyperparameter Optimization ◽  
Histopathology Images

A Similarity-Based Object Classification Using Deep Neural Networks

2019 ◽  
pp. 197-219
Author(s):  
Parvathi R. ◽  
Pattabiraman V.
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Search Algorithm ◽  
Random Projection ◽  
Learning Networks ◽  
Learning Models ◽  
External Conditions ◽  
And Training

This chapter proposes a hybrid method for classification of the objects based on deep neural network and a similarity-based search algorithm. The objects are pre-processed with external conditions. After pre-processing and training different deep learning networks with the object dataset, the authors compare the results to find the best model to improve the accuracy of the results based on the features of object images extracted from the feature vector layer of a neural network. RPFOREST (random projection forest) model is used to predict the approximate nearest images. ResNet50, InceptionV3, InceptionV4, and DenseNet169 models are trained with this dataset. A proposal for adaptive finetuning of the deep learning models by determining the number of layers required for finetuning with the help of the RPForest model is given, and this experiment is conducted using the Xception model.

scholarly journals Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4629 ◽  
Author(s):  
Ciaran Cooney ◽  
Attila Korik ◽  
Raffaella Folli ◽  
Damien Coyle
Keyword(s):  
Deep Learning ◽  
Support Vector ◽  
Great Success ◽  
Hyperparameter Optimization ◽  
Linear Discriminant ◽  
Classifier Performance ◽  
Open Question ◽  
The Impact ◽  
Regularized Linear Discriminant Analysis

Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain–computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across several domains. However, it remains an open question whether DL methods provide significant advances over traditional machine learning (ML) approaches for classification of imagined speech. Furthermore, hyperparameter (HP) optimization has been neglected in DL-EEG studies, resulting in the significance of its effects remaining uncertain. In this study, we aim to improve classification of imagined speech EEG by employing DL methods while also statistically evaluating the impact of HP optimization on classifier performance. We trained three distinct convolutional neural networks (CNN) on imagined speech EEG using a nested cross-validation approach to HP optimization. Each of the CNNs evaluated was designed specifically for EEG decoding. An imagined speech EEG dataset consisting of both words and vowels facilitated training on both sets independently. CNN results were compared with three benchmark ML methods: Support Vector Machine, Random Forest and regularized Linear Discriminant Analysis. Intra- and inter-subject methods of HP optimization were tested and the effects of HPs statistically analyzed. Accuracies obtained by the CNNs were significantly greater than the benchmark methods when trained on both datasets (words: 24.97%, p < 1 × 10–7, chance: 16.67%; vowels: 30.00%, p < 1 × 10–7, chance: 20%). The effects of varying HP values, and interactions between HPs and the CNNs were both statistically significant. The results of HP optimization demonstrate how critical it is for training CNNs to decode imagined speech.

scholarly journals Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4373 ◽  
Author(s):  
Zabit Hameed ◽  
Sofia Zahia ◽  
Begonya Garcia-Zapirain ◽  
José Javier Aguirre ◽  
Ana María Vanegas
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Diagnostic System ◽  
Microscopic Analysis ◽  
Classification Performance ◽  
Histopathological Analysis ◽  
Learning Approach ◽  
Ensemble Strategy ◽  
Histopathology Images

Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of 97.73% for carcinoma class and overall accuracy of 95.29%. Also, it offered an F1 score of 95.29%. These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images.

scholarly journals Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mosleh Hmoud Al-Adhaileh ◽  
Ebrahim Mohammed Senan ◽  
Waselallah Alsaade ◽  
Theyazn H. H Aldhyani ◽  
Nizar Alsharif ◽  
...  
Keyword(s):  
Deep Learning ◽  
Feature Vector ◽  
Gastrointestinal Diseases ◽  
Activation Function ◽  
Learning Networks ◽  
Video Endoscopy ◽  
Deep Feature ◽  
Short Period

Currently, nearly two million patients die of gastrointestinal diseases worldwide. Video endoscopy is one of the latest technologies in the medical imaging field for the diagnosis of gastrointestinal diseases, such as stomach ulcers, bleeding, and polyps. Medical video endoscopy generates many images, so doctors need considerable time to follow up all the images. This creates a challenge for manual diagnosis and has encouraged investigations into computer-aided techniques to diagnose all the generated images in a short period and with high accuracy. The novelty of the proposed methodology lies in developing a system for diagnosis of gastrointestinal diseases. This paper introduces three networks, GoogleNet, ResNet-50, and AlexNet, which are based on deep learning and evaluates them for their potential in diagnosing a dataset of lower gastrointestinal diseases. All images are enhanced, and the noise is removed before they are inputted into the deep learning networks. The Kvasir dataset contains 5,000 images divided equally into five types of lower gastrointestinal diseases (dyed-lifted polyps, normal cecum, normal pylorus, polyps, and ulcerative colitis). In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. The softmax activation function receives the deep feature vector and classifies the input images into five classes. All CNN models achieved superior results. AlexNet achieved an accuracy of 97%, sensitivity of 96.8%, specificity of 99.20%, and AUC of 99.98%.

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