Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action. Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study's findings show that our proposed strategy outperforms existing models significantly. Thus, these models could be applicable in clinics to support the doctor in the diagnosis of cancers.
Lung cancer is one of the death threatening diseases among human beings. Early and accurate detection of lung cancer can increase the survival rate from lung cancer. Computed Tomography (CT) images are commonly used for detecting the lung cancer.Using a data set of thousands of high-resolution lung scans collected from Kaggle competition [1], we will develop algorithms that accurately determine in the lungs are cancerous or not. The proposed system promises better result than the existing systems, which would be beneficial for the radiologist for the accurate and early detection of cancer. The method has been tested on 198 slices of CT images of various stages of cancer obtained from Kaggle dataset[1] and is found satisfactory results. The accuracy of the proposed method in this dataset is 72.2%
Breast cancer is one of the most common cause of deaths among women. Mammography is a widely used imaging modality that can be used for cancer detection in its early stages. Deep learning is widely used for the detection of cancerous masses in the images obtained via mammography. The need to improve accuracy remains constant due to the sensitive nature of the datasets so we introduce segmentation and wavelet transform to enhance the important features in the image scans. Our proposed system aids the radiologist in the screening phase of cancer detection by using a combination of segmentation and wavelet transforms as pre-processing augmentation that leads to transfer learning in neural networks. The proposed system with these pre-processing techniques significantly increases the accuracy of detection on Mini-MIAS.
The diagnosed cases of Breast cancer is increasing annually and unfortunately getting converted into a high mortality rate. Cancer, at the early stages, is hard to detect because the malicious cells show similar properties (density) as shown by the non-malicious cells. The mortality ratio could have been minimized if the breast cancer could have been detected in its early stages. But the current systems have not been able to achieve a fully automatic system which is not just capable of detecting the breast cancer but also can detect the stage of it. Estimation of malignancy grading is important in diagnosing the degree of growth of malicious cells as well as in selecting a proper therapy for the patient. Therefore, a complete and efficient clinical decision support system is proposed which is capable of achieving breast cancer malignancy grading scheme very efficiently. The system is based on Image processing and machine learning domains. Classification Imbalance problem, a machine learning problem, occurs when instances of one class is much higher than the instances of the other class resulting in an inefficient classification of samples and hence a bad decision support system. Therefore EUSBoost, ensemble based classifier is proposed which is efficient and is able to outperform other classifiers as it takes the benefits of both-boosting algorithm with Random Undersampling techniques. Also comparison of EUSBoost with other techniques is shown in the paper.
Digitization of histopathology slides has led to several advances, from easy data sharing and collaborations to the development of digital diagnostic tools. Deep learning (DL) methods for classification and detection have shown great potential, but often require large amounts of training data that are hard to collect, and annotate. For many cancer types, the scarceness of data creates barriers for training DL models. One such scenario relates to detecting tumor metastasis in lymph node tissue, where the low ratio of tumor to non-tumor cells makes the diagnostic task hard and time-consuming. DL-based tools can allow faster diagnosis, with potentially increased quality. Unfortunately, due to the sparsity of tumor cells, annotating this type of data demands a high level of effort from pathologists. Using weak annotations from slide-level images have shown great potential, but demand access to a substantial amount of data as well. In this study, we investigate mitigation strategies for limited data access scenarios. Particularly, we address whether it is possible to exploit mutual structure between tissues to develop general techniques, wherein data from one type of cancer in a particular tissue could have diagnostic value for other cancers in other tissues. Our case is exemplified by a DL model for metastatic colon cancer detection in lymph nodes. Could such a model be trained with little or even no lymph node data? As alternative data sources, we investigate 1) tumor cells taken from the primary colon tumor tissue, and 2) cancer data from a different organ (breast), either as is or transformed to the target domain (colon) using Cycle-GANs. We show that the suggested approaches make it possible to detect cancer metastasis with no or very little lymph node data, opening up for the possibility that existing, annotated histopathology data could generalize to other domains.
Deep learning object detection algorithm has been widely used in medical image analysis. Currently all the object detection tasks are based on the data annotated with object classes and their bounding boxes. On the other hand, medical images such as mammography usually contain normal regions or objects that are similar to the lesion region, and may be misclassified in the testing stage if they are not taken care of. In this paper, we address such problem by introducing a novel top likelihood loss together with a new sampling procedure to select and train the suspected target regions, as well as proposing a similarity loss to further identify suspected targets from targets. Mean average precision (mAP) according to the predicted targets and specificity, sensitivity, accuracy, AUC values according to classification of patients are adopted for performance comparisons. We firstly test our proposed method on a private dense mammogram dataset. Results show that our proposed method greatly reduce the false positive rate and the specificity is increased by 0.25 on detecting mass type cancer. It is worth mention that dense breast typically has a higher risk for developing breast cancers and also are harder for cancer detection in diagnosis, and our method outperforms a reported result from performance of radiologists. Our method is also validated on the public Digital Database for Screening Mammography (DDSM) dataset, brings significant improvement on mass type cancer detection and outperforms the most state-of-the-art work.
Medical imaging is the most important tool for detecting complications in the inner body of medicine. Nowadays, with the development of image processing technology as well as changing the size of photos to higher resolution images in the field of digital medical imaging, there is an efficient and accurate system for segmenting this. Real-world images that for a variety of reasons have poor heterogeneity, noise and contrast are essential. Digital image segmentation in medicine is used for diagnostic and therapeutic analysis, which is very helpful for physicians. In this study, we aim at liver cancer photographs, which aim to more accurately detect the lesion or tumor of the liver because accurate and timely detection of the tumor is very important in the survival and life of the patient.The aim of this paper is to simplify the obnoxious study problems related to the study of MR images. The liver is the second organ most generic involved by metastatic disease being liver cancer one of the prominent causes of death worldwide. Without healthy liver a person cannot survive. It is life threatening disease which is very challenging perceptible for both medical and engineering technologists. Medical image processing is used as a non-invasive method to detect tumours. The chances of survival having liver Tumor highly depends on early detection of Tumor and then classification as cancerous and noncancerous tumours. Image processing techniques for automatic detection of brain are includes pre-processing and enhancement, image segmentation, classification and volume calculation, Poly techniques have been developed for the detection of liver Tumor and different liver toM oR detection algorithms and methodologies utilized for Tumor diagnosis. Novel methodology for the detection and diagnosis of liver Tumor.
In the last decade, researchers working in the domain of computer vision and Artificial Intelligence (AI) have beefed up their efforts to come up with the automated framework that not only detects but also identifies stage of breast cancer. The reason for this surge in research activities in this direction are mainly due to advent of robust AI algorithms (deep learning), availability of hardware that can train those robust and complex AI algorithms and accessibility of large enough dataset required for training AI algorithms. Different imaging modalities that have been exploited by researchers to automate the task of breast cancer detection are mammograms, ultrasound, magnetic resonance imaging, histopathological images or any combination of them. This article analyzes these imaging modalities and presents their strengths, limitations and enlists resources from where their datasets can be accessed for research purpose. This article then summarizes AI and computer vision based state-of-the-art methods proposed in the last decade, to detect breast cancer using various imaging modalities. Generally, in this article we have focused on to review frameworks that have reported results using mammograms as it is most widely used breast imaging modality that serves as first test that medical practitioners usually prescribe for the detection of breast cancer. Second reason of focusing on mammogram imaging modalities is the availability of its labeled datasets. Datasets availability is one of the most important aspect for the development of AI based frameworks as such algorithms are data hungry and generally quality of dataset affects performance of AI based algorithms. In a nutshell, this research article will act as a primary resource for the research community working in the field of automated breast imaging analysis.
Skin cancer continues to be the most frequently diagnosed form of cancer in the U.S., with not only significant effects on health and well-being but also significant economic costs associated with treatment. A crucial step to the treatment and management of skin cancer is effective skin cancer detection due to strong prognosis when treated at an early stage, with one of the key screening approaches being dermoscopy examination. Motivated by the advances of deep learning and inspired by the open source initiatives in the research community, in this study we introduce CancerNet-SCa, a suite of deep neural network designs tailored for the detection of skin cancer from dermoscopy images that is open source and available to the general public as part of the Cancer-Net initiative. To the best of the authors' knowledge, CancerNet-SCa comprises of the first machine-designed deep neural network architecture designs tailored specifically for skin cancer detection, one of which possessing a self-attention architecture design with attention condensers. Furthermore, we investigate and audit the behaviour of CancerNet-SCa in a responsible and transparent manner via explainability-driven model auditing. While CancerNet-SCa is not a production-ready screening solution, the hope is that the release of CancerNet-SCa in open source, open access form will encourage researchers, clinicians, and citizen data scientists alike to leverage and build upon them.
Microwave-based breast cancer detection has been proposed as a complementary approach to compensate for some drawbacks of existing breast cancer detection techniques. Among the existing microwave breast cancer detection methods, machine learning-type algorithms have recently become more popular. These focus on detecting the existence of breast tumours rather than performing imaging to identify the exact tumour position. A key step of the machine learning approaches is feature extraction. One of the most widely used feature extraction method is principle component analysis (PCA). However, it can be sensitive to signal misalignment. This paper presents an empirical mode decomposition (EMD)-based feature extraction method, which is more robust to the misalignment. Experimental results involving clinical data sets combined with numerically simulated tumour responses show that combined features from EMD and PCA improve the detection performance with an ensemble selection-based classifier.
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