List of Articles Cancer

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  1 - Mitosis detection in breast cancer histological images based on texture features using AdaBoost
  Sooshiant  Zakariapour Hamid Jazayeri Mehdi Ezoji
  10.7508/jist.2017.18.003
  Counting mitotic figures present in tissue samples from a patient with cancer, plays a crucial role in assessing the patient’s survival chances. In clinical practice, mitotic cells are counted manually by pathologists in order to grade the proliferative activity of brea More

  Counting mitotic figures present in tissue samples from a patient with cancer, plays a crucial role in assessing the patient’s survival chances. In clinical practice, mitotic cells are counted manually by pathologists in order to grade the proliferative activity of breast tumors. However, detecting mitoses under a microscope is a labourious, time-consuming task which can benefit from computer aided diagnosis. In this research we aim to detect mitotic cells present in breast cancer tissue, using only texture and pattern features. To classify cells into mitotic and non-mitotic classes, we use an AdaBoost classifier, an ensemble learning method which uses other (weak) classifiers to construct a strong classifier. 11 different classifiers were used separately as base learners, and their classification performance was recorded. The proposed ensemble classifier is tested on the standard MITOS-ATYPIA-14 dataset, where a pixel window around each cells center was extracted to be used as training data. It was observed that an AdaBoost that used Logistic Regression as its base learner achieved a F1 Score of 0.85 using only texture features as input which shows a significant performance improvement over status quo. It also observed that "Decision Trees" provides the best recall among base classifiers and "Random Forest" has the best Precision. Manuscript profile
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  2 - Diagnosis of Gastric Cancer via Classification of the Tongue Images using Deep Convolutional Networks
  Elham Gholami Seyed Reza Kamel Tabbakh Maryam khairabadi
  10.52547/jist.9.35.191
  Gastric cancer is the second most common cancer worldwide, responsible for the death of many people in society. One of the issues regarding this disease is the absence of early and accurate detection. In the medical industry, gastric cancer is diagnosed by conducting nu More

  Gastric cancer is the second most common cancer worldwide, responsible for the death of many people in society. One of the issues regarding this disease is the absence of early and accurate detection. In the medical industry, gastric cancer is diagnosed by conducting numerous tests and imagings, which are costly and time-consuming. Therefore, doctors are seeking a cost-effective and time-efficient alternative. One of the medical solutions is Chinese medicine and diagnosis by observing changes of the tongue. Detecting the disease using tongue appearance and color of various sections of the tongue is one of the key components of traditional Chinese medicine. In this study, a method is presented which can carry out the localization of tongue surface regardless of the different poses of people in images. In fact, if the localization of face components, especially the mouth, is done correctly, the components leading to the biggest distinction in the dataset can be used which is favorable in terms of time and space complexity. Also, since we have the best estimation, the best features can be extracted relative to those components and the best possible accuracy can be achieved in this situation. The extraction of appropriate features in this study is done using deep convolutional neural networks. Finally, we use the random forest algorithm to train the proposed model and evaluate the criteria. Experimental results show that the average classification accuracy has reached approximately 73.78 which demonstrates the superiority of the proposed method compared to other methods. Manuscript profile
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  3 - Using Combined Classifier Based on the Separation of Conventional and Unconventional Samples to Diagnose Breast Cancer
  amin rezaeipanah hesam vaghebin
  Breast cancer is one of the most common types of cancers in women and in recent years there has been a significant increase in the number of people with this disease. With the increasing spread of science, data mining has become one of the most widely used areas for imp More

  Breast cancer is one of the most common types of cancers in women and in recent years there has been a significant increase in the number of people with this disease. With the increasing spread of science, data mining has become one of the most widely used areas for improving therapeutic systems. In this paper, the diagnosis of breast cancer is performed in two steps. In the first step, an improved genetic algorithm is used to identify the desirable features in the prediction of this disease, and in the second stage, conventional and Unconventional samples are identified to increase the accuracy and create the final classification model. For classification work, a comparison between two decision tree and Support vector machine model is used to show the results of the superiority of the Support vector machine model. The results of the experiments reported the accuracy of breast cancer diagnosis on WBCD, WDBC and WPBC data sets are 99.26%, 98.55% and 98.45%, respectively. Manuscript profile
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  4 - Breast Cancer Classification Approaches - A Comparative Analysis
  Mohan Kumar Sunil Kumar Khatri Masoud Mohammadian
  10.52547/jist.33395.11.41.1
  Cancer of the breast is a difficult disease to treat since it weakens the patient's immune system. Particular interest has lately been shown in the identification of particular immune signals for a variety of malignancies in this regard. In recent years, several methods More

  Cancer of the breast is a difficult disease to treat since it weakens the patient's immune system. Particular interest has lately been shown in the identification of particular immune signals for a variety of malignancies in this regard. In recent years, several methods for predicting cancer based on proteomic datasets and peptides have been published. The cells turns into cancerous cells because of various reasons and get spread very quickly while detrimental to normal cells. In this regard, identifying specific immunity signs for a range of cancers has recently gained a lot of interest. Accurately categorizing and compartmentalizing the breast cancer subtype is a vital job. Computerized systems built on artificial intelligence can substantially save time and reduce inaccuracy. Several strategies for predicting cancer utilizing proteomic datasets and peptides have been reported in the literature in recent years.It is critical to classify and categorize breast cancer treatments correctly. It's possible to save time while simultaneously minimizing the likelihood of mistakes using machine learning and artificial intelligence approaches. Using the Wisconsin Breast Cancer Diagnostic dataset, this study evaluates the performance of various classification methods, including SVC, ETC, KNN, LR, and RF (random forest). Breast cancer can be detected and diagnosed using a variety of measurements of data (which are discussed in detail in the article) (WBCD). The goal is to determine how well each algorithm performs in terms of precision, recall, and accuracy. The variation of each classification threshold has been tested on various algorithms and SVM turned out to be very promising. Manuscript profile
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  5 - The efficacy of self-advocacy training on effective communication of adolescents with chemotherapy / radiotherapy induced alopecia
  niloofar naghash Afrooz Afshari batool ahadi
  The aim of this study was determining the efficacy of self-advocacy training on communication skills of adolescents with chemotherapy/ radiotherapy induced alopecia. The method of present study was quasi experimental single-subject with pre-test, post-test and two-week More

  The aim of this study was determining the efficacy of self-advocacy training on communication skills of adolescents with chemotherapy/ radiotherapy induced alopecia. The method of present study was quasi experimental single-subject with pre-test, post-test and two-week and one-month follow-ups. The statistical population of the study consisted of all adolescents aged 12-15 years with chemotherapy/ radiotherapy alopecia in Tehran in 1400. The sample group consisted of 4 adolescents with chemotherapy/ radiotherapy alopecia, who were selected by available sampling method and taking into account the inclusion criteria. The self-advocacy training intervention program was implemented in 5 sessions and the participants responded to the communication skills questionnaire (Attarha, 2007) in two stages, the baseline (A) and the experimental stage (B). SPSS and T test were used for data analysis, and t-test results illustrated the significant effect of self-advocacy training on communication skills and its subscales in adolescents with alopecia due to chemotherapy/ radiotherapy. Therefore, it can be concluded that self-advocacy training due to self-awareness, communication skills and leadership training can lead to the promotion of communication skills of adolescents with alopecia due to chemotherapy/ radiotherapy. Manuscript profile
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  6 - Identification of Cancer-Causing Genes in Gene Network Using Feedforward Neural Network Architecture
  مصطفی اخوان صفار abbas ali rezaee
  Identifying the genes that initiate cancer or the cause of cancer is one of the important research topics in the field of oncology and bioinformatics. After the mutation occurs in the cancer-causing genes, they transfer it to other genes through protein-protein interact More

  Identifying the genes that initiate cancer or the cause of cancer is one of the important research topics in the field of oncology and bioinformatics. After the mutation occurs in the cancer-causing genes, they transfer it to other genes through protein-protein interactions, and in this way, they cause cell dysfunction and the occurrence of disease and cancer. So far, various methods have been proposed to predict and classify cancer-causing genes. These methods mostly rely on genomic and transcriptomic data. Therefore, they have a low harmonic mean in the results. Research in this field continues to improve the accuracy of the results. Therefore, network-based methods and bioinformatics have come to the aid of this field. In this study, we proposed an approach that does not rely on mutation data and uses network methods for feature extraction and feedforward three-layer neural network for gene classification. For this purpose, the breast cancer transcriptional regulatory network was first constructed. Then, the different features of each gene were extracted as vectors. Finally, the obtained vectors were given to a feedforward neural network for classification. The obtained results show that the use of methods based on multilayer neural networks can improve the accuracy and harmonic mean and improve the performance compared to other computational methods. Manuscript profile