In this paper, the brief survey of data mining classification by using the machine learning techniques is presented. The machine learning techniques like decision tree and support vector machine play the important role in all the applications of artificial intelligence. Decision tree works efficiently with discrete data and SVM is capable of building the nonlinear boundaries among the classes. Both of these techniques have their own set of strengths which makes them suitable in almost all classification tasks.

Medical image analysis and classification is an important application of computer vision wherein disease prediction based on an input image is provided to assist healthcare professionals. There are many deep learning architectures that accept the different medical image modalities and provide the decisions about the diagnosis of various cancers, including breast cancer, cervical cancer, etc. The Pap-smear test is the commonly used diagnostic procedure for early identification of cervical cancer, but it has a high rate of false-positive results due to human error. Therefore, computer-aided diagnostic systems based on deep learning need to be further researched to classify the pap-smear images accurately. A fuzzy min–max neural network is a neuro fuzzy architecture that has many advantages, such as training with a minimum number of passes, handling overlapping class classification, supporting online training and adaptation, etc. This paper has proposed a novel hybrid technique that combines the deep learning architectures with machine learning classifiers and fuzzy min–max neural network for feature extraction and Pap-smear image classification, respectively. The deep learning pretrained models used are Alexnet, ResNet-18, ResNet-50, and GoogleNet. Benchmark datasets used for the experimentation are Herlev and Sipakmed. The highest classification accuracy of 95.33% is obtained using Resnet-50 fine-tuned architecture followed by Alexnet on Sipakmed dataset. In addition to the improved accuracies, the proposed model has utilized the advantages of fuzzy min–max neural network classifiers mentioned in the literature.

Cervical cancer is the second most commonly seen cancer in women. It affects the cervix portion of the vagina. The most preferred diagnostic test required for screening cervical cancer is the pap smear test. Pap smear is a time-consuming test as it requires detailed analysis by expert cytologists. Cytologists can screen around 100 to 1000 slides depending upon the availability of advanced equipment. Due to this reason Artificial intelligence (AI) based computer-aided diagnosis system for the classification of pap smear images is needed. There are some AI-based solutions proposed in the literature, still an effective and accurate system is under research. In this paper, the deep learning-based hybrid methodology namely DeepCyto is proposed for the classification of pap smear cytology images. The DeepCyto extracts the feature fusion vectors from pre-trained models and passes these to two workflows. Workflow-1 applies principal component analysis and machine learning ensemble to classify the pap smear images. Workflow-2 takes feature fusion vectors as an input and applies an artificial neural network for classification. The experiments are performed on three benchmark datasets namely Herlev, SipakMed, and LBCs. The performance measures of accuracy, precision, recall and F1-score are used to evaluate the effectiveness of the DeepCyto. The experimental results depict that Workflow-2 has given the best performance on all three datasets even with a smaller number of epochs. Also, the performance of the DeepCyto Workflow 2 on multi-cell images of LBCs is better compared to single cell images of other datasets. Thus, DeepCyto is an efficient method for accurate feature extraction as well as pap smear image classification.

Cervical cancer causes the abnormal growth of the cells at the cervix region of the woman’s uterus. It is the second most common type of cancer after the breast cancer seen in women. Detection of cervical cancer at the early stage is vital. Various screening methods like Pap smear, Colposcopy and HPV testing are carried out for the detection of cervical cancer. The possible screening techniques to diagnose cervical cancer include the visual inspection of the cervix (VIA), Pap smear examination (cytology), Colposcopy, Biopsy and HPV-DNA detection. All these techniques need the involvement of an expert doctor and/or pathologist. Also, a cancer diagnosis is a subjective process where the experience and training of pathologists are significant factors. Intelligent and automated screening systems will be helpful in such scenarios. This paper presents the methodology for cervical cancer prediction based on pap smear images. Pre-trained deep neural network models are used for feature extraction and different machine-learning (ML) models are trained on extracted features. In the proposed methodology, four pre-trained models such as Alexnet, Resnet-18, Resnet-50 and Googlenet are fine-tuned for feature extraction followed by the different ML algorithms. Among these simple logistic regression, the algorithms have performed best with the highest accuracy of 95.14% with the Alexnet pre-trained model.

Global health has been seriously threatened due to the rapid spread of the Coronavirus disease. In some cases, patients with high risk require early detection. Considering the less RT-PCR sensitivity as a screening tool, medical imaging techniques like computed tomography (CT) provide great advantages when compared. To reduce the fatality CT or X-ray image diagnosis plays an important role. To lessen the burden of radiologists in this global health crisis use of computer-aided diagnosis is crucial. As a reason, automated image segmentation is also of great benefit for clinical resolution assistance in quantitative research and health monitoring. This paper presents an approach of CT (Computed Tomography) Segmentation of lung images using the U-Net architecture.