A Comparative Study of Different Deep Learning Architectures for Benign-Malignant Mass Classification

Kulkarni, Sujata; Rabidas, Rinku

doi:10.1007/978-981-16-6407-6_66

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Additionally, its configuration and tuning are a hard and lengthy process. Optimizing the deep learning architecture parameters requires too many experiments running for long periods to set the optimal settings [ 45 , 46 , 47 ].…”

Section: Results and Discussionmentioning

confidence: 99%

“…Additionally, optimizing DL architecture parameters requires plenty of experiments using deep learning networks and architecture combinations. The dataset utilized in this work is small and is not enough to adopt a deep learning approach [ 15 , 45 , 46 , 47 , 48 , 49 , 50 ]. The results section compares the proposed technique for verification and performance evaluation against recent deep learning architectures with different deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

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Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation and Classification Utilizing Small Datasets

Yousef

Flora

Iftekharuddin

2022

Sensors

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The work presented here develops a computer vision framework that is view angle independent for vehicle segmentation and classification from roadway traffic systems installed by the Virginia Department of Transportation (VDOT). An automated technique for extracting a region of interest is discussed to speed up the processing. The VDOT traffic videos are analyzed for vehicle segmentation using an improved robust low-rank matrix decomposition technique. It presents a new and effective thresholding method that improves segmentation accuracy and simultaneously speeds up the segmentation processing. Size and shape physical descriptors from morphological properties and textural features from the Histogram of Oriented Gradients (HOG) are extracted from the segmented traffic. Furthermore, a multi-class support vector machine classifier is employed to categorize different traffic vehicle types, including passenger cars, passenger trucks, motorcycles, buses, and small and large utility trucks. It handles multiple vehicle detections through an iterative k-means clustering over-segmentation process. The proposed algorithm reduced the processed data by an average of 40%. Compared to recent techniques, it showed an average improvement of 15% in segmentation accuracy, and it is 55% faster than the compared segmentation techniques on average. Moreover, a comparative analysis of 23 different deep learning architectures is presented. The resulting algorithm outperformed the compared deep learning algorithms for the quality of vehicle classification accuracy. Furthermore, the timing analysis showed that it could operate in real-time scenarios.

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Section: Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation and Classification Utilizing Small Datasets

Yousef

Flora

Iftekharuddin

2022

Sensors

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“…Ting et al [26] proposed to extract hierarchical features from a custom CNN and used a set of images from the Mammographic Image Analysis Society (MIAS) [27]. Kulkarni et al [28] comparatively studied different DL architectures for mass classification by using 200 mass images from DDSM. Li et al [29] proposed a two-view mammogram images classification model composed of two branches of CNNs for feature extraction and two modified ResNets for feature fusion on images from DDSM.…”

Section: Related Workmentioning

confidence: 99%

On the Applicability of Prototypical Part Learning in Medical Images: Breast Masses Classification Using ProtoPNet

Carloni¹,

Berti²,

Iacconi³

et al. 2023

Lecture Notes in Computer Science

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Deep learning models have become state-of-theart in many areas, ranging from computer vision to marine and agriculture research. However, concerns have been raised regarding the transparency of their decisions, especially in the image domain. In this regard, Explainable Artificial Intelligence has been gaining popularity in recent years. The ProtoPNet model, which breaks down an image into prototypes and uses evidence gathered from the prototypes to classify an image, represents an appealing approach. Still, questions regarding its effectiveness arise when the application domain changes from real-world natural images to gray-scale medical images. This work explores the applicability of prototypical part learning in medical imaging by experimenting with ProtoPNet on a breast masses classification task. The two aspects we considered to evaluate the applicability of this approach were the classification capabilities and the validity of explanations. We looked for the optimal model's hyperparameters configuration by operating a random search. We trained the model in a five-fold CV supervised framework, with mammogram images cropped around the lesions and ground-truth labels of benign/malignant masses. Then, we compared the performance metrics of ProtoPNet to that of the corresponding base architecture, which was ResNet18, trained under the same framework. In addition, an experienced radiologist provided a clinical viewpoint on the quality of the learned prototypes, the patch activations, and the global explanations. We achieved a Recall of 0.769 and area under the receiver operating characteristic curve of 0.719 in our experiments. Even though our findings are non-optimal for entering the clinical practice yet, the radiologist found ProtoPNet's explanations very intuitive, reporting a high level of satisfaction. Therefore, we believe that prototypical part learning offers a reasonable and promising trade-off between classification performance and the quality of the related explanation.

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DF-dRVFL: A novel deep feature based classifier for breast mass classification

Ren

Guttery

et al. 2023

Multimed Tools Appl

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Amongst all types of cancer, breast cancer has become one of the most common cancers in the UK threatening millions of people’s health. Early detection of breast cancer plays a key role in timely treatment for morbidity reduction. Compared to biopsy, which takes tissues from the lesion for further analysis, image-based methods are less time-consuming and pain-free though they are hampered by lower accuracy due to high false positivity rates. Nevertheless, mammography has become a standard screening method due to its high efficiency and low cost with promising performance. Breast mass, as the most palpable symptom of breast cancer, has received wide attention from the community. As a result, the past decades have witnessed the speeding development of computer-aided systems that are aimed at providing radiologists with useful tools for breast mass analysis based on mammograms. However, the main issues of these systems include low accuracy and require enough computational power on a large scale of datasets. To solve these issues, we developed a novel breast mass classification system called DF-dRVFL. On the public dataset DDSM with more than 3500 images, our best model based on deep random vector functional link network showed promising results through five-cross validation with an averaged AUC of 0.93 and an average accuracy of $$81.71\%$$ 81.71 % . Compared to sole deep learning based methods, average accuracy has increased by 0.38. Compared with the state-of-the-art methods, our method showed better performance considering the number of images for evaluation and the overall accuracy.

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A Comparative Study of Different Deep Learning Architectures for Benign-Malignant Mass Classification

Cited by 5 publications

References 16 publications

Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation and Classification Utilizing Small Datasets

Monocular Camera Viewpoint-Invariant Vehicular Traffic Segmentation and Classification Utilizing Small Datasets

On the Applicability of Prototypical Part Learning in Medical Images: Breast Masses Classification Using ProtoPNet

DF-dRVFL: A novel deep feature based classifier for breast mass classification

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