Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models

Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of 97.73% for carcinoma class and overall accuracy of 95.29%. Also, it offered an F1 score of 95.29%. These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images

Characterisation of ictal and interictal states of epilepsy: A system dynamic approach of principal dynamic modes analysis.

Epilepsy is a brain disorder characterised by the recurrent and unpredictable interruptions of normal brain function, called epileptic seizures. The present study attempts to derive new diagnostic indices which may delineate between ictal and interictal states of epilepsy. To achieve this, the nonlinear modeling approach of global principal dynamic modes (PDMs) is adopted to examine the functional connectivity of the temporal and frontal lobes with the occipital brain segment using an ensemble of paediatric EEGs having the presence of epileptic seizure. The distinct spectral characteristics of global PDMs are found to be in line with the neural rhythms of brain dynamics. Moreover, we find that the linear trends of associated nonlinear functions (ANFs) associated with the 2nd and 4th global PDMs (representing delta, theta and alpha bands) of Fp1-F3 may differentiate between ictal and interictal states of epilepsy. These findings suggest that global PDMs and their associated ANFs may offer potential utility as diagnostic neural measures for ictal and interictal states of epilepsy

The estimated five global PDMs for the T7–P7 (left panels) and Fp1–F3 (right panels) inputs in connection with P3–O1 (as an output) in the time- (top panels) and frequency-domain (bottom panels).

<p>PDMs, principal dynamic modes.</p

The estimated cubic ANFs for the ictal state of one representative subject of the training data set corresponding to the five global PDMs (shown in Fig 2) for the T7–P7 (upper panels) and Fp1–F3 (bottom panels), along with their best linear fits (red line).

<p>ANFs, associated nonlinear functions; PDMs, principal dynamic modes.</p

The estimated five global PDMs for the T7–P7 (left panels) and Fp1–F3 (right panels) inputs in connection with P3–O1 (as an output) in the time- (top panels) and frequency-domain (bottom panels).

<p>PDMs, principal dynamic modes.</p

The ensemble averages of estimated cubic ANFs along with their standard deviation bounds for the T7–P7 (top panels) and Fp1–F3 (bottom panels) for the interictal states of the training data set.

<p>The solid lines represent means and dotted lines represent standard deviation bounds. Coefficients of cubic ANFs were utilized to determine the mean and standard deviation bounds. ANFs, associated nonlinear functions.</p

Scatter-plot of estimated linear gains coefficients of ANFs corresponding to the 2nd and 4th global PDMs for the Fp1–F3 for interictal and ictal states of the training data set.

<p>The classification line has been obtained using a linear discriminator, and shows no false-negatives and no false-positives. ANFs, associated nonlinear functions; PDMs, principal dynamic modes.</p

The ensemble averages of estimated linear gain coefficients (i.e., slopes of best linear lines fitted to cubic ANFs) for the T7–P7 (upper panel) and Fp1–F3 (bottom panel) for interictal and ictal states of the training data set.

<p>No significant changes were found across any ANF of either input for ictal versus interictal states of the training data set (<i>p</i> > 0.05, paired <i>t</i>-test). The error bars represent standard deviation. ANFs, associated nonlinear functions.</p

The ensemble averages of estimated linear gain coefficients (i.e., slopes of best linear lines fitted to cubic ANFs) for the T7–P7 (upper panel) and Fp1–F3 (bottom panel) for interictal and ictal states of the test data set.

<p>No significant changes were found across any ANF of either input for ictal versus interictal states of the test data set (<i>p</i> > 0.05, paired <i>t</i>-test). The error bars represent standard deviation. ANFs, associated nonlinear functions.</p

The block diagram of a dual-input global PDM model with five global PDMs.

<p>In the present study, the T7–P7 and Fp1–F3 channels are taken as input 1 and input 2, respectively, and the P3–O1 channel is considered as a model output. The ANFs are cubic polynomials. Only significant cross-terms are included in the final model. PDM, principal dynamic mode.</p