Objective: Automated Pap smear cervical screening is one of the most effective imaging based cancer detection tools used for categorizing cervical cell images as normal and abnormal. Traditional classification methods depend on hand-engineered features and show limitations in large, diverse datasets. Effective feature extraction requires an efficient image preprocessing and segmentation, which remains prominent challenge in the field of Pathology. In this paper, a deep learning concept is used for cell image classification in large datasets. Methods: This relatively proposed novel method, combines abstract and complicated representations of data acquired in a hierarchical architecture. Convolution Neural Network (CNN) learns meaningful kernels that simulate the extraction of visual features such as edges, size, shape and colors in image classification. A deep prediction model is built using such a CNN network to classify the various grades of cancer: normal, mild, moderate, severe and carcinoma. It is an effective computational model which uses multiple processing layers to learn complex features. A large dataset is prepared for this study by systematically augmenting the images in Herlev dataset. Result: Among the three sets considered for the study, the first set of single cell enhanced original images achieved an accuracy of 94.1% for 5 class, 96.2% for 4 class, 94.8% for 3 class and 95.7% for 2 class problems. The second set includes contour extracted images showed an accuracy of 92.14%, 92.9%, 94.7% and 89.9% for 5, 4, 3 and 2 class problems. The third set of binary images showed 85.07% for 5 class, 84% for 4 class, 92.07% for 3 class and highest accuracy of 99.97% for 2 class problems. Conclusion: The experimental results of the proposed model showed an effective classification of different grades of cancer in cervical cell images, exhibiting the extensive potential of deep learning in Pap smear cell image classification.
Supplementation of water for irrigation in needed in south India due to uncertainty of monsoon rainfall. This paper proposes a support system to manage the irrigation system based on the information provided by humidity, temperature, soil moisture and weather information. The temperature, humidity and soil moisture data were collected by sensors. The proposed ANFIS based system consists of N inputs and a single output which determines the irrigation time needed for the crop. The experimentation is carried out using real time data collected from the region of VALLAM, located near THANJAVUR. The result helps in determining the time for irrigation which helps in increasing the yield of the crop.
Computer aided categorization of smear images has been considered challenging in the past few decades. Cervical cancer is a main cause of mortality among women worldwide and more prevalent in underdeveloped countries. This disease can be successfully treated, even fully cured, if detected at its early phase. Computerized image analysis methods are primarily of great interest as they provide significant benefits for clinicians with reliable and timely diagnosis of the samples. Dedicated image analysis algorithms provide mathematical description of the region of interest which provide a great support to pathologists for decision making. In this review, we have outlined state of the art techniques expressed in prominent publications on computer assisted diagnostic system for cancer detection. By utilizing the domain aspects of cervical cancer, suitable methods and techniques are explored and presented. This review also presents a knowledge to assess the methodology used in the literature and emphasized some of the inadequacies and weaknesses in the reviewed methods. The study accentuates the future directions pertinent to the development of cost-effective, automated disease classification system that should be a significant advantage for countries with limited resources and treatment services.
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