Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images

Murcia-Gómez, David; Rojas-Valenzuela, Ignacio; Valenzuela, Olga

doi:10.3390/app122211375

Cited by 14 publications

(8 citation statements)

References 66 publications

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“…All images in the "C-Signatures" and "Cedar Signature" dataset used in the study were resized to 120 pixels × 120 pixels and trained. In image recognition and classification, preprocessing steps significantly affect the performance of the proposed CNN models [25]. To improve the performance of the Si-CNN and Si-CNN+NC models, every image in both datasets was preprocessed.…”

Section: Datasets Construction and Workflow Of The Methodsmentioning

confidence: 99%

Evaluation of Classification Performance of New Layered Convolutional Neural Network Architecture on Offline Handwritten Signature Images

Ozkan,

Erdogmus

2024

Symmetry

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While there are many verification studies on signature images using deep learning algorithms in the literature, there is a lack of studies on the classification of signature images. Signatures are used as a means of identification for banking, security controls, symmetry, certificates, and contracts. In this study, the aim was to design network architectures that work very fast in areas that require only signature images. For this purpose, a new Si-CNN network architecture with existing layers was designed. Afterwards, a new loss function and layer (Si-CL), a novel architecture using Si-CL as classification layer in Si-CNN to increase the performance of this architecture, was designed. This architecture was called Si-CNN+NC (New Classification). Si-CNN and Si-CNN+NC were trained with two datasets. The first dataset which was used for training is the “C-Signatures” (Classification Signatures) dataset, which was created to test these networks. The second dataset is the “Cedar” dataset, which is a benchmark dataset. The number of classes and sample numbers in the two datasets are symmetrical with each other. To compare the performance of the trained networks, four of the most well-known pre-trained networks, GoogleNet, DenseNet201, Inceptionv3, and ResNet50, were also trained with the two datasets with transfer learning. The findings of the study showed that the proposed network models can learn features from two different handwritten signature images and achieve higher accuracy than other benchmark models. The test success of the trained networks showed that the Si-CNN+NC network outperforms the others, in terms of both accuracy and speed. Finally, Si-CNN and Si-CNN+NC networks were trained with the gold standard dataset MNIST and showed superior performance. Due to its superior performance, Si-CNN and Si-CNN+NC can be used by signature experts as an aid in a variety of applications, including criminal detection and forgery.

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Section: Datasets Construction and Workflow Of The Methodsmentioning

confidence: 99%

Evaluation of Classification Performance of New Layered Convolutional Neural Network Architecture on Offline Handwritten Signature Images

Ozkan,

Erdogmus

2024

Symmetry

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“…Image preprocessing of medical images is crucial before adding images to a neural network, impacting accuracy significantly [39,40]. The proposed image pre-processing technique stood out from existing methods.…”

Section: Image Preprocessingmentioning

confidence: 99%

DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features

Rana,

Hosen,

Tonni

et al. 2024

Sensors

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Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification.

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“…To guarantee that the dataset was clean and consistent before being utilized for training and evaluation, several preprocessing measures had to be taken [37]. The initial step was to eliminate any damaged or unnecessary data [38]. This is a crucial step because inaccurate or unnecessary data can harm the model's performance [39].…”

Section: Data Preprocessingmentioning

confidence: 99%

“…After the preprocessing steps were completed, the dataset was split into three sets: a training set, a validation set, and a test set. The ratio used was 80:20, with 80% of the data being used for training and the remaining 20% being split evenly between the validation and test sets [38]. This is a common split ratio, as it allows for enough data to be used for training while still leaving enough data for validation and testing.…”

Section: Data Preprocessingmentioning

confidence: 99%

Deep learning model enhanced skin cancer detection

Kolachina

Agada²,

Li³

et al. 2023

Biophotonics and Immune Responses XVIII

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According to the American Academy of Dermatology Association, identifying the types of skin cancer depends on the origin of a cell mutation resulting in the rapid growth of these abnormal cells in the epidermis. These mutations lead the skin cells to multiply rapidly and form malignant tumors. Skin cancer is ranked as the 17th most common form of cancer worldwide according to the World Cancer Research Fund. Skin cancer treatments cost the United States more than $8 billion (about $25 per person in the US) each year, making skin cancer the fifth most costly cancer for Medicare. Furthermore, skin cancer is an under recognized problem for diverse populations, including young women and minorities. Researchers have been exploring different technologies to detect skin care at its early stage to avoid high mortality rate and expensive medical treatment. This paper presents a novel ensembled deep learning model for the early detection of skin cancer. Our research is based on The HAM10000 dataset, a diverse collection of multi-sourced dermatoscopic images of common pigmented skin lesions which consists of 10015 dermatoscopic images. We have compared several deep learning neural network architectures and classifiers such as DNN, RNN, SVM and KNN in terms of accuracy rate and computation complexity and presented an ensembled deep learning model for early skin cancer detection. The main contribution of this paper is the productions of a comparative study of several skin cancer detection techniques using powerful computer vision techniques and deep learning models and a novel ensembled deep learning model for skin cancer detection.

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Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images

Cited by 14 publications

References 66 publications

Evaluation of Classification Performance of New Layered Convolutional Neural Network Architecture on Offline Handwritten Signature Images

Evaluation of Classification Performance of New Layered Convolutional Neural Network Architecture on Offline Handwritten Signature Images

DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features

Deep learning model enhanced skin cancer detection

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