Computer-aided Cancer Detection Research Articles

A Comprehensive assessment of Convolutional Neural Networks for skin and oral cancer detection using medical images

Early detection is essential to effectively treat two of the most prevalent cancers, skin and oral. Deep learning approaches have demonstrated promising results in effectively detecting these cancers using Computer-Aided Cancer Detection (CAD) and medical imagery. This study proposes a deep learning-based method for detecting skin and oral cancer using medical images. We discuss various Convolutional Neural Network (CNN) models such as AlexNet, VGGNet, Inception, ResNet, DenseNet, and Graph Neural Network (GNN). Image processing techniques such as image resizing and image filtering are applied to skin cancer and oral cancer images to improve the quality and remove noise from images. Data augmentation techniques are used next to expand the training dataset and strengthen the robustness of the CNN model. The best CNN model is selected based on the training accuracy, training loss, validation accuracy, and validation loss. The study shows DenseNet achieves state-of-the-art performance on the skin cancer dataset.

Toward a more nuanced understanding of probability estimation biases.

In real life, we often have to make judgements under uncertainty. One such judgement task is estimating the probability of a given event based on uncertain evidence for the event, such as estimating the chances of actual fire when the fire alarm goes off. On the one hand, previous studies have shown that human subjects often significantly misestimate the probability in such cases. On the other hand, these studies have offered divergent explanations as to the exact causes of these judgment errors (or, synonymously, biases). For instance, different studies have attributed the errors to the neglect (or underweighting) of the prevalence (or base rate) of the given event, or the overweighting of the evidence for the individual event ('individuating information'), etc. However, whether or to what extent any such explanation can fully account for the observed errors remains unclear. To help fill this gap, we studied the probability estimation performance of non-professional subjects under four different real-world problem scenarios: (i) Estimating the probability of cancer in a mammogram given the relevant evidence from a computer-aided cancer detection system, (ii) estimating the probability of drunkenness based on breathalyzer evidence, and (iii & iv) estimating the probability of an enemy sniper based on two different sets of evidence from a drone reconnaissance system. In each case, we quantitatively characterized the contributions of the various potential explanatory variables to the subjects' probability judgements. We found that while the various explanatory variables together accounted for about 30 to 45% of the overall variance of the subjects' responses depending on the problem scenario, no single factor was sufficient to account for more than 53% of the explainable variance (or about 16 to 24% of the overall variance), let alone all of it. Further analyses of the explained variance revealed the surprising fact that no single factor accounted for significantly more than its 'fair share' of the variance. Taken together, our results demonstrate quantitatively that it is statistically untenable to attribute the errors of probabilistic judgement to any single cause, including base rate neglect. A more nuanced and unifying explanation would be that the actual biases reflect a weighted combination of multiple contributing factors, the exact mix of which depends on the particular problem scenario.

3D Inception U-net with Asymmetric Loss for Cancer Detection in Automated Breast Ultrasound.

Breast cancer is the most common cancer and the leading cause of cancer-related deaths for women all over the world. Recently, automated breast ultrasound (ABUS) has become a new and promising screening modality for whole breast examination. However, reviewing volumetric ABUS is time-consuming and lesions could be missed during the examination. Therefore, computer-aided cancer detection in ABUS volume is extremely expected to help clinician for the breast cancer screening. We develop a novel end-to-end 3D convolutional network for automated cancer detection in ABUS volume, in order to accelerate reviewing and meanwhile to provide high detection sensitivity with low false positives (FPs). Specifically, an efficient 3D Inception Unet-style architecture with fusion deep supervision mechanism is proposed to attain decent detection performance. In addition, a novel asymmetric loss is designed to help the network balancing false positive and false negative regions, thus improving detection sensitivity for small cancerous lesions. The efficacy of our network was extensively validated on a dataset including 196 patients with 661 cancer regions. Our network obtained a detection sensitivity of 95.1% with 3.0 FPs per ABUS volume. Furthermore, the average inference time of the network was 0.1 second per volume, which largely shortens the conventional reviewing time. The proposed network provides efficient and accurate cancer detection scheme using ABUS volume, and may assist clinicians for more efficient breast cancer screening.

Entropy-Based Breast Cancer Detection in Digital Mammograms Using World Cup Optimization Algorithm

Breast cancer is one of the deadliest cancers for women. Early detection of skin cancer gives a high chance for the women to escape from the malady and obtain a cure at the initial stages. In other words, early detection of breast cancer has a direct relation by the women's quality of life. In this case, mammography images are important. Indeed, the main test used for screening and early diagnosis of breast cancer is mammography. In recent years, computer-aided cancer detection has been turned into an active field of research and showed a promising future. In this study, a new optimization algorithm based on thresholding is introduced. A WCO algorithm is employed as the optimization algorithm. WCO is a new meta-heuristic approach which is inspired by the FIFA world cup challenge. The presented method utilizes random samples as candidate solutions from the search space inside the image histogram with considering to the objective function that is utilized by the Kapur's method.

Latest advances in artificial intelligence, computer-aided cancer detection and machine learning in radiology and oncology

Latest advances in artificial intelligence, computer-aided cancer detection and machine learning in radiology and oncology

A Biopsy Analysis System for Cancer Diagnosis and Prognosis

and prognosis of the cancer can be done through the radiological, surgical and pathological assessment of cancer tissue samples. Cancer detection and grading of biopsy image is the standard clinical practice for the diagnosis and prognosis of cancer. In a large hospital, a pathologist typically handles number of cancer detection cases per day. It is, therefore, a very difficult and time-consuming work. This paper proposes a method for automatic cancer diagnosis and prognosis to assist pathologists by providing second opinions and reducing their workload. Computer-aided cancer detection and grading system for cancer tissue cell nuclei in histological image is introduced and validated as part of the biopsy analysis system. Cancer cell nuclei are selectively stained with monoclonal antibodies, such as the ant estrogen receptor antibodies, which are widely used as part of assessing patient diagnosis and prognosis in breast cancer. Many features used in the analysis of histopathology imagery are inspired by grading features defined by clinical pathologists as important for diagnosis and prognosis. This paper micro cancer object of breast tumor classified using feed forward back propagation Neural Network. Twenty six hundred sets of cell nuclei characteristics obtained by applying cancer detection and cancer image analysis techniques to microscopic slides. The dataset consist of eight features which represent the input layer to the FNN. The FNN will classify the micro cancer object into type4, type3, type2 and type1. The sensitivity, specificity and accuracy were found to be equal 99.64%, 98.54% and 98.80% respectively. It can be concluded that FNN gives fast and accurate classification and it works as promising tool for classification of cancer cell nuclei and grading. The overall accuracy of cancer detection and grading of the proposed system is 96.50%. Thus, this approach is suitable for automated real time breast cancer diagnosis and prognosis tool. KeywordsCell, Histopathology, H & E, Cancer nuclei segmentation, Feature Extraction, Biopsy, Biopsy Image, Neural Network, Cancer Detection, Biopsy Scoring, Biopsy Grading

Mass segmentation using a combined method for cancer detection

BackgroundBreast cancer is one of the leading causes of cancer death for women all over the world and mammography is thought of as one of the main tools for early detection of breast cancer. In order to detect the breast cancer, computer aided technology has been introduced. In computer aided cancer detection, the detection and segmentation of mass are very important. The shape of mass can be used as one of the factors to determine whether the mass is malignant or benign. However, many of the current methods are semi-automatic. In this paper, we investigate fully automatic segmentation method.ResultsIn this paper, a new mass segmentation algorithm is proposed. In the proposed algorithm, a fully automatic marker-controlled watershed transform is proposed to segment the mass region roughly, and then a level set is used to refine the segmentation. For over-segmentation caused by watershed, we also investigated different noise reduction technologies. Images from DDSM were used in the experiments and the results show that the new algorithm can improve the accuracy of mass segmentation.ConclusionsThe new algorithm combines the advantages of both methods. The combination of the watershed based segmentation and level set method can improve the efficiency of the segmentation. Besides, the introduction of noise reduction technologies can reduce over-segmentation.

Automatic breast CAD results for seven categories of abnormalities.

5 Background: This prospective study was conducted to determine efficacy of preclinical use of automatic computer-aided cancer detection (CAD) system. Patients were women of high risk referred for MRI of breast with goals of assessing sensitivity/specificity of an automatic CAD platform which used kinetic and morphological (spiculation, volume, solidity, surface area, elongation, maximum extent) tools. We assessed classification of abnormality using histopathology as our gold standard. Methods: 1,976 patients with 2,149 abnormalities included were scanned from 2007 to 2010. Patient inclusion criteria were women: with dense breasts; BRCA1 or BRCA2 genes; family history of cancer; suspected of having recurrent cancer; anomalous mammogram/ultrasound; pain; or clinical suspicion. All patients included gave institutionally IRB approved HIPAA compliant informed consent. CAD software post-processing was automatically applied to dynamic contrast-enhanced MRI (DCE-MRI). One pre- and 4 post-scan series were used to calculate wash-in/out parameters. System's automatic detection was applied to entire breast(s) volume. No manually placed regions of interest were used. Results: Of 1,976 women scanned, 361 had mastectomies, 17 had bilateral mastectomies, 488 had lumpectomies/excisional biopsies, 623 had core biopsies, 166 had non-surgical approaches; radiation or chemotherapy. Mean tumor resected was 2.95 cm; SD=2.88 cm, while CAD detected breast tumor calculated size was 3.20 cm; SD=2.66 cm. Conclusions: Automatic CAD applied to breast DCE-MRI demonstrated high sensitivity, reasonable specificity, and accurate estimates for location within breast, and volume of malignancy. Automatic 3D CAD translucent display will aid oncology planners in tumor localization, volume and extent estimations. [Table: see text]