Medical Image Classification with Hand-Designed or Machine-Designed Texture Descriptors: A Performance Evaluation

Badejo, Joke A.; Adetiba, Emmanuel; Akinrinmade, Adekunle; Akanle, Matthew B.

doi:10.1007/978-3-319-78759-6_25

Cited by 19 publications

(3 citation statements)

References 27 publications

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“…Deniz et al [16] used a fine-tuning technique and found that fine-tuning the last layer of pretrained AlexNet outperformed the SVM classifier that used the feature fusion of both pretrained AlexNet and VGG16. Badejo et al [17] discovered that LBP outperformed AlexNet when comparing hand-crafted feature descriptors to the extracted features of pretrained AlexNet. For the first time, a magnification-independent binary classification (MIB) was introduced in [28].…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Study on Classification of Breast Cancer Histopathological Images: Binary Versus Multi-Category and Magnification-Specific Versus Magnification-Independent

Taheri,

Golrizkhatami,

Basabrain

et al. 2024

IEEE Access

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There are millions of cancer cases worldwide every year, and breast cancer is one of the most prevalent diseases with the highest mortality rate. The manual effort of pathologists can be significantly reduced by computerized diagnostic systems, which improve the accuracy and reliability of diagnosis. In this paper, we present four novel systems for breast cancer diagnosis in four different scenarios: binary versus multi-class classification and magnification-specific (MS) versus magnification-independent (MI) classification. In each of the proposed systems, we developed an automatic score-level fused CNN model using a pretrained deep neural network and named it the Multi-Level Feature Fusion (MLF2) model. The MLF2-CNN, similar to the conventional CNN models, integrates the feature extraction and classification phases of BC classification into a single automatic learning procedure. Additionally, MLF2-CNN performs an automatic score-level fusion of several classifiers that were trained with multi-level features to make the final decision. A pretrained DenseNet-121 is selected as the backbone of the proposed MLF2-CNN, and several new links are added to the CNN architecture to capture multi-stage features. Several experiments on the publicly available BreakHis dataset demonstrate that the proposed systems capture the best descriptive features and outperform state-of-the-art techniques in most of the scenarios.

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Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Study on Classification of Breast Cancer Histopathological Images: Binary Versus Multi-Category and Magnification-Specific Versus Magnification-Independent

Taheri,

Golrizkhatami,

Basabrain

et al. 2024

IEEE Access

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“…These recognition systems were implemented using custom CNNs with the structure captured in Table 5. CNN was considered the tool for the development of the model in this work because it has been proven to yield better performances compared with hand-crafted methods especially large-scale data samples [48]. Finally, to improve the performance of the voice recognition system, the concept of voting was adopted for the predictions of spectrograms generated from an utterance.…”

Section: Unimodal Systems Designmentioning

confidence: 99%

NaijaFaceVoice: A Large-Scale Deep Learning Model and Database of Nigerian Faces and Voices

et al. 2023

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The fusion of two or more traits in multimodal biometrics generally improves recognition accuracy. The question is, by how much? Large-scale databases are better suited for training deep learning models for better generalization and accuracy. Therefore, a large-scale multimodal database is beneficial. However, publicly available large-scale multimodal databases are scarce, especially for faces and voices. Again, because a face image is 2-D while a voice is 1-D, there is the challenge of the best way to fuse both. Therefore, improvements owing to fusion have hitherto yielded marginal improvements. This study proposes a semi-automated curation algorithm for the extraction of the faces and voices of target individuals in videos to create a large-scale face-voice database. The curation technique involves observing the positions at the time of the occurrence of the target subject's faces and voices in videos. These positions are supplied to a MATLAB2017b script that detects the faces in the observed regions, crops, resizes, autolabels, and writes them to the disk. A second MATLAB2017b script, extracts the audio content within the observed regions, auto-labels, and writes the voice segments to the disk. The created database named NaijaFaceVoice consists of 2,656 subjects with over 2 million faces and 195 hours of utterances. The database was employed to develop a large-scale recognition system that leveraged Convolutional Neural Networks. Robust fusion methods incorporating the proposed Spectrogram-Voting concept significantly improved performance achieving a record equal error rate of 0.0003519%, an improvement by a factor of over 450.

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“…A deep CNN consists of an input layer that contains image data of m training examples, multiple hidden layers that compute features from input images and an output layer, which classifies the learned images. Deep learning models employ non-linear transformation functions to solve complex large-scale problems (Reyes et al, 2015;Li et al, 2016;Badejo et al, 2018). As shown in Figure 1, the hidden layers consist of stacked convolution layers that convolve using a Rectified Linear Unit (ReLU) activation (or transfer) function, as well as a pooling layer, which reduces the dimension of the convoluted image.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

LeafsnapNet: An Experimentally Evolved Deep Learning Model for Recognition of Plant Species based on Leafsnap Image Dataset

Ajayi¹,

Kala²,

Ajala³

et al. 2021

Journal of Computer Science

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Medical Image Classification with Hand-Designed or Machine-Designed Texture Descriptors: A Performance Evaluation

Cited by 19 publications

References 27 publications

A Comprehensive Study on Classification of Breast Cancer Histopathological Images: Binary Versus Multi-Category and Magnification-Specific Versus Magnification-Independent

A Comprehensive Study on Classification of Breast Cancer Histopathological Images: Binary Versus Multi-Category and Magnification-Specific Versus Magnification-Independent

NaijaFaceVoice: A Large-Scale Deep Learning Model and Database of Nigerian Faces and Voices

LeafsnapNet: An Experimentally Evolved Deep Learning Model for Recognition of Plant Species based on Leafsnap Image Dataset

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