Epithelium detection and cervical intraepithelial neoplasia classification in digitized histology images

“Cervical cancer is one of the most deadly cancers faced by women. It is the second leading cause of cancer death in women aged 20 to 39 years. In order to detect cancer at early stages, pathologists analyze the epithelium region from the cervical histology images. These histology images have a pre-cervical cancer condition called cervical intraepithelial neoplasia (CIN) determined by pathologists. This study deals with automating the process of epithelium detection and epithelium CIN classification in digitized histology images. For epithelium detection, the objective is to detect epithelium regions in microscopy images from non-epithelium regions and background. convolutional neural networks, both shallow and deep networks are used for epithelium detection. The highest epithelium detection accuracy of 98.84% is obtained using transfer learning on VGG-19 architecture, pre-trained on the ImageNet dataset. For CIN classification, the epithelium region is divided into 5 segments along the medial axis and patches from each segment were used for training the deep learning model. Vertical segment level classification probabilities from deep learning model are obtained and further classified using SVM, LDA, MLP, logistic and RF classifiers. The highest image level accuracy obtained is 77.27% for MLP classifier using voting”--Abstract, page iii

The Characteristic of Productive Women’s Interest In Early Detection of Cervical Cancer by Using IVA

The Characteristic of Productive Women’s Interest In Early Detection of Cervical Cancer by Using IV

Human Intestinal Condition Identification based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier

Colon cancer is a type of disease that attacks the intestinal walls cell of humans. Colorectal endoscopic screening technique is a common step carried out by the health expert/gynecologist to determine the condition of the human intestine. Manual interpretation requires quite a long time to reach a result. Along with the development of increasingly advanced digital computing techniques, then some of the weaknesses of the manually endoscopic image interpretation analysis model can be corrected by automating the detection process of the presence or absence of cancerous cells in the gut. Identification of human intestinal conditions using an artificial neural network method with the blended input feature produces a higher accuracy value compared to the artificial neural network with the non-blended input feature. The difference in classifier performance produced between the two is quite significant, that is equal to 0.065 (6.5%) for accuracy; 0.074 (7.4%) for recall; 0.05 (5.0%) for precision; and 0.063 (6.3%) for f-measure

Computer-aided diagnosis of low grade endometrial stromal sarcoma (LGESS)

Low grade endometrial stromal sarcoma (LGESS) accounts for about 0.2% of all uterine cancer cases. Approximately 75% of LGESS patients are initially misdiagnosed with leiomyoma, which is a type of benign tumor, also known as fibroids. In this research, uterine tissue biopsy images of potential LGESS patients are preprocessed using segmentation and stain normalization algorithms. We then apply a variety of classic machine learning and advanced deep learning models to classify tissue images as either benign or cancerous. For the classic techniques considered, the highest classification accuracy we attain is about 0.85, while our best deep learning model achieves an accuracy of approximately 0.87. These results clearly indicate that properly trained learning algorithms can aid in the diagnosis of LGESS

TelsNet: temporal lesion network embedding in a transformer model to detect cervical cancer through colposcope images

Cervical cancer ranks as the fourth most prevalent malignancy among women globally. Timely identification and intervention in cases of cervical cancer hold the potential for achieving complete remission and cure. In this study, we built a deep learning model based on self-attention mechanism using transformer architecture to classify the cervix images to help in diagnosis of cervical cancer. We have used techniques like an enhanced multivariate gaussian mixture model optimized with mexican axolotl algorithm for segmenting the colposcope images prior to the Temporal Lesion Convolution Neural Network (TelsNet) classifying the images. TelsNet is a transformer-based neural network that uses temporal convolutional neural networks to identify cancerous regions in colposcope images. Our experiments show that TelsNet achieved an accuracy of 92.7%, with a sensitivity of 73.4% and a specificity of 82.1%. We compared the performance of our model with various state-of-the-art methods, and our results demonstrate that TelsNet outperformed the other methods. The findings have the potential to significantly simplify the process of detecting and accurately classifying cervical cancers at an early stage, leading to improved rates of remission and better overall outcomes for patients globally

Automatic segmentation of whole-body bone scintigrams as a preprocessing step for computer assisted diagnostics

Bone scintigraphy or whole-body bone scan is one of the most common diagnostic procedures in nuclear medicine used in the last 25 years. Pathological conditions, technically poor quality images and artifacts necessitate that algorithms use su±cient background knowledge of anatomy and spatial relations of bones in order to work satisfactorily. We present a robust knowledge based methodology for detecting reference points of the main skeletal regions that simultaneously processes anterior and posterior whole-body bone scintigrams. Expert knowledge is represented as a set of parameterized rules which are used to support standard image processing algorithms. Our study includes 467 consecutive, non-selected scintigrams, which is to our knowledge the largest number of images ever used in such studies. Automatic analysis of whole-body bone scans using our knowledge based segmentation algorithm gives more accurate and reliable results than previous studies. Obtained reference points are used for automatic segmentation of the skeleton, which is used for automatic (machine learning) or manual (expert physicians) diagnostics. Preliminary experiments show that an expert system based on machine learning closely mimics the results of expert physicians

A Hybrid Linear Iterative Clustering and Bayes Classification-Based GrabCut Segmentation Scheme for Dynamic Detection of Cervical Cancer

Cervical cancer earlier detection remains indispensable for enhancing the survival rate probability among women patients worldwide. The early detection of cervical cancer is done relatively by using the Pap Smear cell Test. This method of detection is challenged by the degradation phenomenon within the image segmentation task that arises when the superpixel count is minimized. This paper introduces a Hybrid Linear Iterative Clustering and Bayes classification-based GrabCut Segmentation Technique (HLC-BC-GCST) for the dynamic detection of Cervical cancer. In this proposed HLC-BC-GCST approach, the Linear Iterative Clustering process is employed to cluster the potential features of the preprocessed image, which is then combined with GrabCut to prevent the issues that arise when the number of superpixels is minimized. In addition, the proposed HLC-BC-GCST scheme benefits of the advantages of the Gaussian mixture model (GMM) on the extracted features from the iterative clustering method, based on which the mapping is performed to describe the energy function. Then, Bayes classification is used for reconstructing the graph cut model from the extracted energy function derived from the GMM model-based Linear Iterative Clustering features for better computation and implementation. Finally, the boundary optimization method is utilized to considerably minimize the roughness of cervical cells, which contains the cytoplasm and nuclei regions, using the GrabCut algorithm to facilitate improved segmentation accuracy. The results of the proposed HLC-BC-GCST scheme are 6% better than the results obtained by other standard detection approaches of cervical cancer using graph cuts